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Why Geospatial Data Should Be Easily Accessible for Every Employee


Unlocking the power of geospatial data can give organizations a competitive edge, from optimizing supply chain logistics and enhancing customer experience to mitigating fraud and improving public health outcomes. But despite its far-reaching benefits, many organizations fail to fully harness geospatial data’s potential.  Why? Because geospatial data is voluminous, complex, and often distributed across multiple […]

The post Why Geospatial Data Should Be Easily Accessible for Every Employee appeared first on DATAVERSITY.


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Author: Rosaria Silipo

Real-Time Data Analytics During Uncertain Times

Are we in a recession? Not in the U.S., according to some economists, a recession is defined as two consecutive quarters of negative gross domestic product (GDP) growth. But most will agree that we are living in uncertain times with the recent failure of two large banks, inflation, widespread layoffs in the technology sector, and geopolitical uncertainty. As a result, the top worry for most CEOs in 2023 is a recession or an economic downturn, according to a recent survey from The Conference Board.

In response to economic pressures, many companies are examining their technology spending more closely, and data analytics is no exception. However, analytics provides the opportunity to deliver more business value than what it costs, and this becomes even more important when an organization’s bottom line is under pressure. Here are just a few areas where data analytics has a huge impact by providing real-time insights that help businesses optimize their operations to increase revenue and cut costs.

Optimizing Pricing and Promotions: By analyzing customer behavior, purchasing patterns, market trends, and competitor pricing, businesses can identify the best pricing strategies and promotional offers to increase sales.

Acquiring and Retaining Customers: Analyzing data can help businesses know their customers better to develop targeted strategies and deliver personalized customer experiences that win new business and prevent customer churn.

Identifying Process Inefficiencies: Data analytics can help businesses detect areas where processes need to be optimized by identifying bottlenecks, and areas where resources are being wasted or where the business is overspending.

Improving Forecasting and Planning:  Businesses can use analytics to predict future sales, which leads to better production planning.

Detecting Fraud:  Detecting fraud with analytics helps avoid financial losses and reduces the costs of investigating and resolving fraud cases.

Reducing Energy Spend: Businesses can analyze energy consumption to reduce energy waste, lowering energy bills.

Increase Employee Productivity:  Analyzing employee data can help identify where employees are over or under-utilized to reduce costs and improve productivity.

Improving Forecasting and Planning:  Businesses can use analytics to predict future sales, which leads to better production and inventory planning.

Assessing and Managing Risks: Risk management analytics helps spot trends and weaknesses and provide insights into the best way to resolve them proactively.

Connect Business Value with the Cost of Business Analytics

Cost does matter. In today’s uncertain times, data analytics initiatives must align costs with business value more than ever before. However, you need to focus on cost optimization rather than cost-cutting. A cost-optimal solution should not only process analytics workloads cost-effectively, but also include data integration, data quality, and other management workloads that add more costs and complexity when sourced from multiple vendors.

The Actian Data Platform provides high business value at low cost. It’s built to maximize resource utilization to deliver unmatched performance and an unbeatable total cost of ownership. Plus, it’s a single platform for data integration, data management, and data analytics. This translates into lower risk, cost, and complexity than cobbling together point solutions.

Watch our webinar, “Maximizing Business Success in an Uncertain World with Real-Time Analytics” to see how the Avalanche platform delivers the analytics decisions makers need in an uncertain world.

The post Real-Time Data Analytics During Uncertain Times appeared first on Actian.


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Author: Teresa Wingfield

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When none is better than bad

Selecting a data management consultant is a critical decision for any organization that aims to effectively manage and leverage its data assets. The value of choosing one you have worked with before cannot be overstated. In this fast-paced digital era, where data is considered the new oil, organizations need an expert who can navigate the complex world of data management and help them extract meaningful insights. Consider some key reasons why selecting a familiar data management consultant is advantageous.

Working with a consultant you have previously collaborated with provides a level of familiarity and trust. Building a strong working relationship takes time, and having prior experience with a consultant ensures a smoother and more efficient process. The consultant already understands your organization’s specific needs, challenges, and goals. They are familiar with your data infrastructure, systems, and processes. This familiarity minimizes the learning curve and enables the consultant to hit the ground running, saving valuable time and resources.

A known data management consultant brings a wealth of contextual knowledge about your organization. They possess insights into your data management history, past projects, and the overall data landscape. This knowledge is invaluable when it comes to identifying potential pitfalls, leveraging existing data assets, and aligning data management strategies with your business objectives. The consultant can provide tailored recommendations and solutions that are aligned with your organization’s unique requirements, resulting in more effective outcomes.

An advantage of working with a consultant you have previously engaged with is their understanding of your organizational culture. Every organization has its own set of values, practices, and communication styles. By selecting a consultant who has worked with your organization before, you ensure a cultural fit. The consultant is aware of your organizational dynamics, decision-making processes, and stakeholder expectations. This familiarity enables them to integrate seamlessly into your team, collaborate effectively, and communicate in a manner that resonates with your organization’s culture, ultimately leading to better outcomes and higher adoption rates of data management initiatives.

A known consultant can leverage their previous experience and successes to drive continuous improvement. They can build upon previous projects, lessons learned, and best practices to optimize your data management processes. By understanding what has worked well in the past, the consultant can identify areas for enhancement and implement strategies to overcome challenges more effectively. This iterative approach ensures that your organization’s data management practices evolve and stay up to date with the latest industry trends, ultimately maximizing the value derived from your data.

Selecting a data management consultant you have worked with before can result in cost savings. Engaging a new consultant often requires investing time and resources in onboarding, training, and knowledge transfer. By choosing a familiar consultant, these expenses can be minimized or even eliminated. The consultant is already familiar with your systems, data models, and workflows, reducing the need for extensive orientation. This efficiency allows you to allocate your budget and resources more effectively, focusing on the actual implementation and execution of data management strategies.

The importance of selecting a data management consultant you have worked with before cannot be overstated. The advantages of familiarity, contextual knowledge, cultural fit, continuous improvement, and cost savings make this decision crucial for successful data management initiatives.

By leveraging the existing relationship and expertise, organizations can enhance their data management capabilities, derive valuable insights, and stay ahead in the data-driven landscape of the modern business world.

Cracking the whip


The bullwhip effect is a phenomenon where small changes in one end of a system can cause large fluctuations in another end of the system. You’ll see it most frequently associated with supply chain and logistics imbalances and fluctuations in demand and supply with amplification of variability in demand.

An adjunctive area of thinking is around the cobweb theorem in economics which relates to market price variability as a result of those same market supply and demand elements. The bullwhip effect can lead to excess inventory, lost revenue, and overinvestment in production whereas the cobweb theorem can lead to radical swings in market prices.

The bullwhip effect and the cobweb theorem are related because they both show how small changes in demand can cause large changes in supply. Ultimately both relate to imperfect information and associative reactions in end-to-end system that are not perfectly aligned or coordinated, leading to overreaction or underreaction by the system participants.

Bullwhip effects and cobweb based pricing in particular often lead to inefficiency, waste, and instability.

Effects on software development

One of the systems that can experience the bullwhip effect is software development. In the process of creating, testing, and deploying software applications that meet the needs and expectations of customers I see many stages and participants. Product managers, sponsors, developers, testers, development managers and of course customers, and users. At each stage the participants have different information and incentives that affect their own decisions and actions.

The bullwhip effect can occur in software development when there is a mismatch between the actual demand for the product or features in the product and the perception of demand in the minds of those within the software organization.

A customer may request a minor enhancement for example but those involved in the development process may interpret this as a major change and vice versa. More or less time and resources may be invested in the development of feature or capability than necessary. Let’s also be clear. Changes may also be triggered by other imperfect information, such as the relationship with the customer’s value in the software house’s revenue contribution chain or the software adoption lifecycle within the customer. Customer X may be a household brand but make a small bottom line contribution or customer Y may be an unknown brand yet have significant economic value to the business and a host of other possible combinations in between.

All of these factors can lead to software product delivery delays, overruns, and potential waste. If the customer requests a major change in the software, but this is underestimated, this can lead to an inadequate results, errors, defects, and customer dissatisfaction and the risk of loss.

Interpretation of requirements is so critical and yet so often overlooked beyond the face value of the ask. Sometimes “the ask” is also founded on anecdotes and opinions instead of evidence, insufficient modelling, the absence of prototyping and minimal market feedback. It is almost as if we fear hearing the critique and are just eager to build a solution.

There is also the proverbial problem of poor communication or handoff of requirements among the various participants. This can lead to distorted information and inaccurate requirements as a whole.

When stakeholders place overly ambitious demands on product or development teams or make decision pivots in an erratic or irregular way instead of making progressive, small incremental changes regularly, you can land up with spikes of activity, abandoning of initiatives in flight and incomplete work. These lead to resourcing and planning confusion, delivery crises and potentially wild cost variations. Everything becomes urgent and predictability on delivery goes out the window in favour of the latest new shiny thing.

Delivery crises or cost and estimate variations introduce uncertainty and anxiousness into the delivery assurance and process and negatively impact the potential usefulness of the roadmap and delivery plans. Promises or suggestions of intent made today settle as dust for tomorrow.

Deals contingent on feature delivery, renewals contingent on feature delivery; omissions of detailed fact, allowing unchallenged assumptions to be made about the presence or capability of features and an over-optimism on actual capabilities relative to real and present functionality encourages and perhaps even induces customers deals but creates feature alignment uncertainty and ruins the best made roadmap plans and planning in general.

Product and engineering managers have seen it time and time again; in start-up software houses it is perhaps the worst of all. Hunger for the commercial deal leads to over promising without due consideration of the impact to roadmap execution plans for existing commitments and other competing customer priorities or issues all in pursuit of business growth.

Demand information, such as feedback or analytics data, that is not shared or not used effectively by the organization as a whole, could have a direct impact on scheduling and resourcing and could result in poor coordination and planning.

Human behaviours, such as greed, exaggeration, or panic can influence offers and commercial decisions especially in times of economic uncertainty or at critical time junctures in the calendar like month, quarter or financial year end to meet quotas or squeeze budgets.

The product backlog

From a backlog perspective, timelines often become elongated, feature backlogs grow and actual product output may crawl, developers may land up producing software features uniquely designed for particular customers or industry segments in response to commercial obligations rather than in alignment with the mission of the business or a given product line’s vision.

There can be loss of revenue too, where features and capabilities are developed in a “misaligned with the market” way. Since developers are often the order takers from product management or executives, they rarely have the real opportunity to dispute the relative priority of things and opportunities may get missed, features may get rushed and the completeness of capability overlooked in favour of a “get it out fast” mindset, all in pursuit of perhaps a box checking exercise or to meet the narrowest of possible needs and expectations quickly.

Feature proliferation without market validation and qualification is effectively overinvestment or misplaced investment. A feature ROI will reveal that some features that seemed great in principle effectively become part of a wasteland of technical debt in production. The features may not be valued and worse, may not be used or adopted at all and removing them may be more expensive than simply having them linger in the product in perpetuity.

Poor quality is often also a result of the bullwhip with developers producing software inconsistently or of a low quality, buggy, unreliable, or incompatible with customer or user expectations. This in turn leads to lowered customer satisfaction, where customers or users are unhappy with the software features they receive or do not receive.

Remediation strategies

The bullwhip effect can be reduced or prevented in software development by adopting some neutralising strategies.

The first of these is an improvement in communication where there is the deliberate use of clear and consistent language, documentation, and feedback without guile or hubris.

This means practical down to earth descriptors that relate to state, opportunity and need. This may be in relation to opportunities, actual customer situations or on the flipside, a changing situation in relation to the state of the technology landscape or resources required to the work. The communication has to go both ways, servicing demand and supply aspects of the business. Developers tell technology stories and account managers tell customer stories. Product managers play back both sides.

Smoothing demand is really about settling on a product execution plan that works for everyone. This is achieved by actually being forward thinking and prescriptive about the product roadmap, features and functions. Providing enough detail to assuage concerns about progress but no so much detail that it becomes a road for the back in terms of execution. General direction of travel and progress markers are what count.

Focusing on intentions for the product based on the core values of the business and how the product lines up against those. The challenge here is for businesses with a small or emerging foothold in the market and a young or small product portfolio that they are trying to evolve. All that said, by choosing a narrow market segment rather than anything and everything with a potential pulse, your software business is focused on where the sales effort investment is likely to yield the best possible opportunities. Customers that are too big may be overbearing, those that are too small may be too expensive to service.

Pricing is often very contentious. A combination between science and the dark arts, it is difficult to always get the price for products perfectly right at the beginning. For this reason, many software products start with a relatively straight forward pricing model that becomes infinitely more complex and sophisticated as the size of the customer opportunity grows and the deals mature. You want to leave just enough money on the table to not feel that you undersold your product.

This may sometimes lead to hockey stick pricing models that seem super affordable or even free at low levels but then grow exponentially according to use or data or something like that – these strategies often leading to debates about value based pricing models. Attempts at price banding, price stepping and the like sometimes help. When these are combined with multiyear discounts or contingencies and other complex calculations, customers and sales people sometimes may get equally confused and the value to cost understanding compromised. This in turn can lead to horse trading or bidding wars, price gouging or ballooning. Poor thinking on pricing can be a destabilizer for investment runways it can also extend them. Be prepared to always rethink your pricing.

Customer churn considerations usually only kick in after the first anniversary of the sale but again, when considered in relation to pricing, feature development, cost to serve and the relative value of the opportunity to the software vendor, these can have an extraordinary and disruptive effect on software development lifecycle management as the customer’s specific backlog requirements get dusted off and get given elevated priority to ensure renewal, all with the limited context of a renewal at risk.

Sharing details on opportunities that have specific needs and expectations should happen regularly. The main participants in this process of communication should be the account teams and the product management team. Nothing should ever be promised to prospects without a clear understanding of the criticality of the requirement to the prospect, the relative position of that thing in a backlog (it may not even exist), the alignment with the product vision and a good understanding of the cost to service the requirement and optimistic timeline. I’d encourage customer teams to regularly involve product management in their customer qualification process also. Product managers crave the indulgence of not just customers but also prospects, as bystanders in the product demos and discovery sessions, they get to hear about the customer context problems and are best positioned to spot opportunities for product improvement or enhancement first hand.

Finally, it is worth considering that all of this relates to managing human behaviours. By educating and motivating all the teams to make rational and informed decisions based on facts rather than emotions one is better positioned to deliver superior software products consistently and affordably. By applying these strategies, software development houses can likely avoid or minimize the bullwhip effect and improve their operational and process efficiency, quality, and customer satisfaction.

Photo Credit: Pexels : Photo by MĂĽĹźerref Ä°kizoÄźlu


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Author: Clinton Jones

Testing and Monitoring Data Pipelines: Part One


Suppose you’re in charge of maintaining a large set of data pipelines from cloud storage or streaming data into a data warehouse. How can you ensure that your data meets expectations after every transformation? That’s where data quality testing comes in. Data testing uses a set of rules to check if the data conforms to […]

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Author: Max Lukichev

Companies Must Have Guardrails in Place When Incorporating Generative AI


At the time of reading this, you’ve likely heard of ChatGPT and/or generative AI and its versatile conversational capabilities. From asking it to draft cohesive blog posts, to generating working computer code, all the way to solving your homework and engaging in discussing world events (as far as they happened before September 2021), it seems […]

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Author: Andrei Papancea

How to Use Cloud Migration as an Opportunity to Modernize Data and Analytics

Ensuring a hassle-free cloud migration takes a lot of planning and working with the right vendor. While you have specific goals that you want to achieve by moving to the cloud, you can also benefit the business by thinking about how you want to expand and optimize the cloud once you’ve migrated. For example, the cloud journey can be the optimal time to modernize your data and analytics.

Organizations are turning to the cloud for a variety of reasons, such as gaining scalability, accelerating innovation, and integrating data from traditional and new sources. While there’s a lot of talk about the benefits of the cloud—and there are certainly many advantages—it’s also important to realize that challenges can occur both during and after migration.

Identify and Solve Cloud Migration Challenges

New research based on surveys of 450 business and IT leaders identified some of the common data and analytics challenges organizations face when migrating to the cloud. They include data privacy, regulatory compliance, ethical data use concerns, and the ability to scale.

One way you can solve these challenges is to deploy a modern cloud data platform that can deliver data integration, scalability, and advanced analytics capabilities. The right platform can also solve another common problem you might experience in your cloud migration—operationalizing as you add more data sources, data pipelines, and analytics use cases.

You need the ability to quickly add new data sources, build pipelines with or without using code, perform analytics at scale, and meet other business needs in a cloud or hybrid environment. A cloud data platform can deliver these capabilities, along with enabling you to easily manage, access, and use data—without ongoing IT assistance.

Use the Cloud for Real-Time Analytics

Yesterday’s analytics approaches won’t deliver the rapid insights you need for today’s advanced automation, most informed decision-making, and the ability to identify emerging trends as they happen to shape product and service offerings. That’s one reason why real-time data analytics is becoming more mainstream.

According to research conducted for Actian, common technologies operational in the cloud include data streaming and real-time analytics, data security and privacy, and data integration. Deploying these capabilities with an experienced cloud data platform vendor can help you avoid problems that other organizations routinely face, such as cloud migrations that don’t meet established objectives or not having transparency into costs, resulting in budget overruns.

Vendor assessments are also important. Companies evaluating vendors often look at the functionality and capabilities offered, the business understanding and personalization of the sales process, and IT efficiency and user experience. A vendor handling your cloud migration should help you deploy the environment that’s best for your business, such as a multi-cloud or hybrid approach, without being locked into a specific cloud service provider.

Once organizations are in the cloud, they are implementing a variety of use cases. The most popular ones, according to research for Actian, include customer 360 and customer analytics, financial risk management, and supply chain and inventory optimization. With a modern cloud data platform, you can bring almost any use case to the cloud.

Drive Transformational Insights Using a Cloud Data Platform

Moving to the cloud can help you modernize both the business and IT. As highlighted in our new eBook “The Top Data and Analytics Capabilities Every Modern Business Should Have,” your cloud migration journey is an opportunity to optimize and expand the use of data and analytics in the cloud. The Avalanche Cloud Data Platform can help. The platform makes it easy for you to connect, manage, and analyze data in the cloud. It also offers superior price performance, and you can use your preferred tools and languages to get answers from your data. Read the eBook to find out more about our research, the top challenges organizations face with cloud migrations, and how to eliminate IT bottlenecks. You’ll also find out how your peers are using cloud platforms for analytics and the best practices for smooth cloud migration.

Related resources you may find useful:

The post How to Use Cloud Migration as an Opportunity to Modernize Data and Analytics appeared first on Actian.


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Author: Brett Martin

How Analysts Leverage Creativity to Gain Powerful Insights from Alternative Data


Creativity is critical to producing superior results in any field, whether it’s the arts, sciences, or technology. The same can be said for alternative data analysis.  The most successful data scientists apply creativity by thinking out of the box when analyzing information to solve challenging problems or understand human behavior. From investment decisions to public policy and […]

The post How Analysts Leverage Creativity to Gain Powerful Insights from Alternative Data appeared first on DATAVERSITY.


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Author: Aleksandras Ĺ ulĹľenko

How Kubernetes Can Help You Weather Regional Disaster Recovery


If you’re lucky, you’ve only had to worry about managing a data disaster recovery effort once or twice in your career, if at all. However, as the rate and number of natural disasters have increased, the chances of needing to navigate through a worst-case scenario have risen. As of April 11, 2023, the U.S. had […]

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Author: Alan Cowles

Unlocking Business Insights: How Supply Chain Analytics Measures Your Company’s Health

In today’s highly competitive business world, companies are constantly looking for ways to improve their supply chain operations. One of the most effective ways to do this is by measuring supply chain performance using real-time analytics. By understanding the performance of each aspect of the supply chain, companies can identify bottlenecks, reduce lead times, and improve customer satisfaction. By implementing real-time supply chain analytics, you can gain valuable insights into your company’s health and identify areas for improvement.

Key Performance Indicators in Supply Chain Analytics

Before diving into the benefits of supply chain analytics, it’s essential to understand the key performance indicators (KPIs) that are typically used to measure supply chain performance. These metrics are vast, but three that are common examples are:

Inventory Turnover: This KPI measures how quickly you are selling your inventory. A low inventory turnover rate can indicate that you are carrying too much inventory, while a high rate can suggest that you are not keeping enough stock on hand.

Order Cycle Time: This KPI measures the time it takes from when a customer places an order to when the order is fulfilled. A longer order cycle time can lead to dissatisfied customers, while a shorter cycle time can improve customer satisfaction.

Perfect Order Rate: This KPI measures the percentage of orders that are delivered on time, in full, and without any errors. A low perfect order rate can indicate that you have issues with your order fulfillment process, which can lead to lost sales and dissatisfied customers.

Using Data Analytics to Improve Supply Chain Performance

One of the most effective ways to improve supply chain performance is using data analytics. By collecting and analyzing data from various aspects of the supply chain, companies can identify patterns and trends that can be used to optimize operations. Data analytics can be used to identify areas where supply chain operations are inefficient or ineffective, such as high inventory levels or long lead times. It can also be used to identify opportunities for improvement, like reducing transportation costs or improving manufacturing efficiency. Some specific areas where supply chain analytics can improve performance include:

  1. Improved forecasting accuracy: By analyzing historical data and trends, you can improve your forecasting accuracy. This can help you better anticipate demand for your products and avoid overstocking or understocking.
  2. Better inventory management: By analyzing inventory turnover and other metrics, you can optimize your inventory levels to reduce carrying costs while still meeting customer demand.
  3. Increased supply chain visibility: By using analytics tools, you can gain more visibility into your supply chain operations. This can help you identify bottlenecks or inefficiencies and make data-driven decisions to improve your supply chain.
  4. Faster order fulfillment: By analyzing order cycle times and perfect order rates, you can identify areas where you can streamline your order fulfillment process. This can help you deliver products to customers faster and improve customer satisfaction.
  5. Reduced risk: By analyzing your supply chain, you can identify potential risks and take steps to mitigate them. For example, you may identify a supplier who is at risk of going out of business, and you can take steps to find a new supplier before a disruption occurs.

Best Practices for Implementing Supply Chain KPIs

Implementing KPIs in a supply chain can be a complex process, but there are several best practices that companies can follow to ensure success. These include:

  1. Defining Clear Objectives: Before implementing KPIs, it’s important to define clear objectives that align with overall business goals. This ensures that KPIs are relevant and meaningful.
  2. Choosing the Right KPIs: Not all KPIs are created equal, and it’s important to choose KPIs that are relevant to specific aspects of the supply chain. This ensures that KPIs provide meaningful insights.
  3. Collecting Accurate, Data: KPIs are only as good as the data that is used to measure them, so it’s important to collect accurate and reliable data. That means that the data must be consistent, complete, and correct, and that data must be available in a timeframe that allows your business to react to changes.
  4. Communicating Results: KPIs should be communicated to all stakeholders in a clear and concise manner. This ensures that everyone understands the importance of KPIs and how they contribute to overall business success.
  5. Continuously Improving: Supply chain operations are constantly evolving, so it’s important to continuously review and improve KPIs to ensure they remain relevant and effective.

By analyzing key performance indicators, businesses can identify inefficiencies, improve customer satisfaction, and reduce costs. Supply chain analytics can provide valuable insights into overall business health when they are built using KPI’s that are directly tied to overall business objectives. Use these resources to learn how the Avalanche Cloud Data Platform is helping to deliver real-time data for supply chain analytics:

The post Unlocking Business Insights: How Supply Chain Analytics Measures Your Company’s Health appeared first on Actian.


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Author: Traci Curran

Why Traditional Threat Prevention Is Insufficient for Insider Threats


Security teams can be so focused on blocking cyberattacks from external actors that they forget the potential threats within their organizations. Verizon reports that insider threats cause almost 20% of all breaches.  Insider threats are difficult to defend against using traditional threat prevention measures because insiders inherently require elevated trust and access to get their jobs done. […]

The post Why Traditional Threat Prevention Is Insufficient for Insider Threats appeared first on DATAVERSITY.


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Author: Anastasios Arampatzis

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Growing Customer Lifetime Value with data

Targeted marketing communications have been available for decades but have been rather exclusively available to only those organizations with the best of systems and most importantly, the best of data.

You’ll want to consider personalized communication to improve customer retention, augment and assure customer satisfaction, support and facilitate cross-selling, and ultimately drive greater customer lifetime value (CLV).

Plenty of market research evidence suggests that personalized communications materially cement and augment the opportunities for an organization to galvanize a strong long-term relationship with the customer and accordingly drive greater CLV.  

with augmented data, there is also now the possibility to identify who the lower and higher-value customers are and identify characteristics and traits that allow improved segmentation leading to greater value growth potential.

When these same augmented data sets are combined with tools like machine learning and artificial intelligence to build predictive models, one is able to formulate strategies to migrate members of lower-value segments or groups to the higher-value ones.

The important fact to consider here is that personalized communication may be the important key. Once you’ve singled out members of lower-value segmented groups, and then embarked on communicating with them in a highly personalized way through direct marketing campaigns, only then are you likely to be able to promote them to the higher groups.

The marketing campaigns of course are not limited to letters in the mail, or personalized emails. Your product marketing mix would also leverage the other elements in the marketing library, namely social media and personalized web experiences. The objective here is to leverage the data to hone the product or service marketing, promotion, and advertising messaging to be very distinctive and almost unique for each and every customer.

Now more so than ever before, the smallest organizations can start to consider the use of the same tools and methods that much greater and older organizations have had at their disposal. Globalization and ready accessibility to the web has meant that any product can be sold almost anywhere where delivery services are possible, further, transparency in pricing, internet search, and aggregators like Expedia, Amazon, Etsy, eBay, and the like mean that customers will do price comparison.

The same products can also be offered in price-differentiated marketplaces if you know who you’re targeting. These benefits also mean that the competitive environment is also greater. Steps need to be taken to differentiate on message and service rather than relying exclusively on the product itself and possibly the dependence on the brand.

In the end, the strength of the relationship that your organization has directly with the customer will provide the best possible predictor for the CLV.

There are many affordable customer relationship management tools available including SaaS-based pay-as-you-grow offerings. Using these together with aligned Master Data Management solutions you are able to analyze and target the right customers and prospects with the right offers.

CLV and in turn, the profitability of each customer leads to measures of value that will be different for different customer groups. The identification of efficiency in the way the value is produced helps to direct an organization’s efforts.  This in turn leads to the refinement of the exact kinds of customers and business partners that your organization wants to expend effort and energy on. The profitability of the customer is the revenue derived from that customer less the costs of identifying, securing, retaining, and growing them. The same approach needs to be used for campaigns. The more compelling the proposition with the greatest possible efficiency, the highest potential profit can be secured per customer.

At Pretectum we believe that these insights and decisions can only be obtained and undertaken from the presence of good and comprehensive master data and we present the Pretectum CMDM as one of the ways to achieve that good master data from origination through controlled curation and distribution.

Your customer data in the cloud

The security of customer data in the cloud depends on several factors, including the security measures implemented by the cloud service provider, the security controls in place for the specific data and the configurations set by the customer.

When customer data is stored in the cloud, it is typically protected by a number of security measures, including: network, physical, encryption, access control, monitoring and auditing. When considering how you manage your customer master data, consider how all these aspects are being handled.

Cloud solutions like the Pretectum CMDM applications are housed in data centers that are secured with access controls, surveillance, and other physical security measures; we make use of the some of the best in class implement network security measures, such as firewalls and data encryption to protect your customer data as it is transmitted over the internet. This data is encrypted both at rest and in transit, to protect it from unauthorized access.

Access to your tenancy and actual data is strictly controlled through role based access controls (RBAC) to ensure that only authorized users can access customer data. In addition, we monitor and audit our systems for security incidents and the potential of attemp[ts to gain unauthorized access to customer data.

Pretectum believe that customer data stored in the cloud is often more secure than data you house in your own systems due to the investment that we make in security infrastructure and personnel, and the implementation of security controls and processes that are designed to protect customer data.

It’s nonetheless important for you to carefully evaluate the security measures we use, and implement additional security controls as needed according to your specific requirements.

Adoption of SaaS based CMDM like the Pretectum CMDM has a number of advantages over on-premise or even private-cloud MDM.

SaaS MDM is typically more affordable as it operates on a subscription-based model and eliminates the need for expensive or dedicated hardware and IT infrastructure The intent with SaaS MDM is to also easily scale up or down to meet changing business needs, making it an ideal solution for organizations that are growing or experiencing changes in the volume of their data.

This approach is also easier to implement and typically require minimal setup and configuration, allowing your organization to quickly realize the benefits of a centralized data management solution. We also continuously update and improve the solution, meaning that our customers always have access to the latest features and security enhancements.

The Pretectum CMDM is also accessible from anywhere with an internet connection, making it easy for employees to access and manage data from different locations and reference the centralized repository for customer master data, improving the quality and accuracy of data across the organization.

To learn more about how you can take advantage of the Pretectum CMDM, contact us to take advantage of a free trial evaluation.

Taking air travel and loyalty to new heights

Airline loyalty programs have become a ubiquitous feature of the aviation industry globally. Most airlines, even the budget carriers, offer loyal travelers a range of benefits, from earning miles towards future flights, to access to airport lounges and priority boarding and seat selection.

But where did it all start? Which airlines were the first to introduce loyalty programs, and how have they evolved since then?

British Overseas Airways Corporation (BOAC), which operated from 1940 to 1974, did not have a formal loyalty program as we know them today. However, the airline did offer various incentives to its frequent flyers and regular customers.

For example, BOAC’s “Comet Club” was a social organization for the airline’s most loyal passengers, which offered exclusive access to special events, promotions, and personalized service. Additionally, BOAC provided perks such as preferential seating and priority boarding to its regular customers.

Concorde, the supersonic jet operated by British Airways and Air France, did not have a formal loyalty program. However, the airlines did offer various incentives to their most loyal customers to encourage repeat business and maintain customer loyalty.

For example, British Airways Concorde Room at London Heathrow airport was an exclusive lounge for Concorde passengers and elite frequent flyers. The lounge offered a luxurious experience with personalized service, fine dining, and other amenities.

Additionally, both British Airways and Air France provided special benefits to their top-tier frequent flyer members, such as priority boarding and access to premium lounges. While these perks were not specific to Concorde travel, they may have been particularly appealing to customers who frequently flew on the supersonic jet.

It’s worth noting that the concept of modern loyalty programs, with their points-based reward systems and other features, did not become widespread until the 1980s and 1990s when American Airlines introduced AAdvantage, the first frequent flyer program.

This was a groundbreaking innovation, offering customers the opportunity to earn miles towards free flights based on the distance they flew. The program was an immediate success, and other airlines quickly followed suit, with Delta introducing its SkyMiles program in 1981, and United launching MileagePlus in 1983.

Air Canada Aeroplan: Air Canada launched its Aeroplan loyalty program in 1984, making it one of the first airline loyalty programs in the world. The program offers rewards for flights with Air Canada and its partners, as well as for purchases made with Aeroplan’s partners.

British Airways Executive Club: British Airways launched its Executive Club loyalty program in 1985. The program offers rewards for flights with British Airways and its partners, as well as for purchases made with the program’s partners.

Qantas Frequent Flyer: Qantas, Australia’s national airline, launched its Frequent Flyer loyalty program in 1987. The program offers rewards for flights with Qantas and its partners, as well as for purchases made with the program’s partners.

Air France-KLM Flying Blue: Air France and KLM merged in 2004 and launched their joint loyalty program, Flying Blue, in 2005. The program offers rewards for flights with Air France, KLM, and their partners, as well as for purchases made with the program’s partners.

Lufthansa Miles & More: Lufthansa, Germany’s national airline, launched the Miles & More loyalty program in 1993. The program offers rewards for flights with Lufthansa and its partners, as well as for purchases made with the program’s partners.

Pan American World Airways (Pan Am) also had a loyalty program called Pan Am WorldPass. It was also launched in the early 1980s and allowed members to earn points for flights on Pan Am and its partner airlines. Members could redeem their points for free flights, upgrades, and other rewards.

These early loyalty programs were relatively simple, offering customers the opportunity to earn miles towards free flights, with additional benefits such as access to airport lounges and priority boarding added later. However, as the airline industry became more competitive, loyalty programs became increasingly sophisticated, offering a wider range of benefits and rewards to customers.

Today, airline loyalty programs are complex systems, with multiple tiers and a range of benefits designed to incentivize customers to fly more frequently and spend more money with the airline. These benefits can include free flights, upgrades, lounge access, priority check-in and boarding, bonus miles, and discounts on hotel bookings, car rentals, and other travel-related services.

One of the most significant developments in airline loyalty programs in recent years has been the growth of the internet and the rise of travel aggregators such as Expedia, Kayak, and Skyscanner. These platforms allow customers to compare prices and book flights from multiple airlines, making it easier than ever to find the cheapest flights.

This has presented a challenge for airlines, as they have had to find ways to compete with these platforms and maintain customer loyalty in a highly competitive market. One way they have done this is by partnering with travel aggregators, allowing customers to earn loyalty points when booking flights through these platforms.

Another approach has been to make loyalty programs more flexible and transparent, allowing customers to earn and redeem points across a wider range of airlines and travel partners. For example, many airlines now offer co-branded credit cards that allow customers to earn points towards flights and other rewards when making purchases outside of travel.

Some airlines have even gone further, offering loyalty programs that are not tied to flights at all. For example, Air France-KLM’s Flying Blue program allows members to earn points through a range of activities, including hotel stays, car rentals, and dining.

However, while airlines have been successful in adapting their loyalty programs to the changing market, travel aggregators continue to pose a threat to these programs allowing customers to compare prices and book flights from multiple airlines, these platforms make it easier for customers to find the cheapest flights, regardless of loyalty programs.

To counter this threat, airlines have had to find ways to make their loyalty programs more attractive to customers. This has included offering more personalized rewards and benefits, such as free flights and upgrades based on individual travel patterns and preferences.

Airlines also leverage the power of social media, with many airlines offering special promotions and rewards to customers who engage with their brand on platforms such as Facebook and Twitter.

By analyzing customer data, airlines can gain insights into customer behavior, preferences, and purchasing habits. This data can be used to personalize offers and rewards to customers, which can help to increase loyalty and encourage repeat business. For example, if an airline knows that a customer frequently travels to a particular destination for business, they may offer that customer bonus points for booking a flight to that destination.

Customer data can also be used to identify trends and patterns in customer behavior, which can inform marketing and business decisions. For example, if an airline notices that a large number of customers are booking flights to a particular destination during a certain time of year, they may adjust their pricing or schedule to better serve that demand.

Overall, customer data is critical to airline loyalty programs because it allows airlines to better understand and serve their customers, which can lead to increased loyalty and revenue.

If your business believes a loyalty program will help with growing your customer relationship then you’ll need a good source for customer data – Pretectum CMDM can help with your customer master data management needs. 

Give Your Customers a Personal Experience by Leveraging Customer Intent


As a business owner or marketing professional, you have to understand the concept of customer intent and how it can be best used for personalized customer experience.

By understanding what it is, businesses can create more personalized marketing offers and tailor their strategies to customers on an individual level. Today, Jones Associates shares a quick post that opens up the doors for you to use customer intent in your marketing endeavors.

What Is Customer Intent?

Customer intent simply means your customers’ motivation behind a certain action. Their intent is what they want to accomplish. For example, if someone searches for women’s shoes, they likely want to buy women’s shoes. You can utilize Google Trends to get an idea of popular searches, but your webmaster can give you more information specific to your site. Intent does not always translate initially into a purchase. Sometimes, your customers’ intent is simply to gather information, such as pricing or size and availability.

How To Leverage Customer Intent

There are many different ways that you can get to know your customers’ underlying motivation.

To fully understand customer intent, you have to do research. You might send surveys or create a focus group. You can also do what’s known as social listening, which is simply the act of monitoring and analyzing social media conversations. You can also utilize data culled from your website to get a snapshot of your customers’ journeys. For example, if they put a pair of shoes in their shopping cart, you know that their intent is, ultimately, to purchase shoes.

If you extrapolate this to an ecommerce store, obviously the site has to be user-friendly with customizable content and an easy-to-use payment section, as well as the ability to analyze sales and inventory data to better streamline each customer’s experience. Luckily, there are options for commerce software that allow you to do just that.

Tailored Content Converts

Grazitti Interactive says it best, “Custom content is the future of marketing.” Why? Because customers don’t want to feel like they are nothing but an open wallet. Customizing your content helps set you apart from the competition and helps your customers throughout their buying journey. Nine out of 10 users rate custom content as more helpful than generic information. Think of it this way: when you reach customers with the info and insight they want, you become a more trustworthy source that shows that you truly know what they need.

Customer Retention Matters

Custom content is not only important when targeting new customers. Constant Content explains that your customized marketing materials also reinforce your image and create a stronger following of loyal customers. Everyone wants more customers, but don’t ever lose sight of the fact that simply increasing your customer retention rate by 5% can boost your revenue by up to 95%. This is not only accomplished through increased sales but also because happy customers tell their friends and family.

Ultimately, offering your customers curated content based on their actions and intent is one of the best ways to offer real value to those that support your business. Remember, custom-tailored content converts more customers, and it’s widely considered more helpful than generic pop-ups and other content. To get the most out of your efforts, make sure to listen to your customers, use data to inform your business decisions, and keep organized and accurate records of your findings.

If you’re looking for a global management consultant who can help you meet and exceed your goals, contact Jones Associates today to get started.


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Author: Uli Lokshin

Cloud Architecture Mistakes: The Perils of Poor Security Architecture


In this five-part series, I’m taking a hard look at the common – and costly – mistakes organizations typically make while building a cloud architecture. Part one explained how organizations can quickly lose visibility and control over their data processing, and detailed how to avoid that mistake. Part two looked at why a DIY approach often goes wrong, […]

The post <strong>Cloud Architecture Mistakes: The Perils of Poor Security Architecture</strong> appeared first on DATAVERSITY.


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Author: Shahzad Ali

7 Best Practices for Data Collection in 2023


Digital procedures play an important role in modern business, as they generate lots of valuable information that can be used to improve organizations and advance their goals. Thus, in 2023, website data collection will be a staple for most growing firms in various industries. However, as with any craft, there are bad, good, and better ways to collect website […]

The post 7 Best Practices for Data Collection in 2023 appeared first on DATAVERSITY.


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Author: Anas Baig

7 Data Engineering Projects To Put On Your Resume


Starting new data engineering projects can be challenging. Data engineers can get stuck on finding the right data for their data engineering project or picking the right tools. And many of my Youtube followers agree as they confirmed in a recent poll that starting a new data engineering project was difficult. Here were the key…
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The post 7 Data Engineering Projects To Put On Your Resume appeared first on Seattle Data Guy.


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Author: research@theseattledataguy.com

Top Bottlenecks to Data Management Platform Adoption

A data management platform (DMP) collects, manages, and analyzes data. This may sound just like a data analytics platform, but a DMP’s scope and purpose are more specific. It gathers audience data which is information about people who respond to advertisements or visit websites or other digital properties.  The DMP uses this data to build anonymized customer profiles that drive targeted digital advertising and personalization.

Using a DMP helps accurately target advertising to the right audience, which results in higher response rates, increased brand recognition, and ultimately, higher conversion rates. But many factors can slow DMP adoption, including:

  1. Low Relevancy. Nothing will slow the adoption of a DMP more than data that does not meet users’ business needs. This can happen when data lacks meaning or when data isn’t timely. For example, first-party data (data your company has collected directly from its audience) often requires enrichment to be useful.
  2. Bad Data. Lack of quality data is one of the main reasons audience data isn’t used when planning campaigns for digital media. In particular, the reliability of third-party data, information collected by companies that don’t have a direct relationship with consumers, is highly variable. Digital marketers who rely on data to help them make important marketing decisions need to know that they can trust its integrity. If data isn’t accurate, complete, consistent, reliable, and up-to-date, users will lose confidence in the DMP and stop using it.
  3. Third-party Cookies. DMPs have historically depended on third-party data. With third-party cookies going away, many are uncertain of the DMP’s future. Some businesses are implementing a zero-party data strategy where a customer intentionally and proactively shares data to fill the third-party data void.
  4. Poor Usability. Data analytics users have traditionally been technically savvy data engineers and data scientists who represent a small percentage of an organization’s employees. Organizations struggle to bring in a broader base of business users, such as marketing teams, when the DMP is hard to use.
  5. Limited Scalability. Scalability is a critical capability for DMP success, but many platforms are unable to expand with growing data volumes and users.
  6. Data Silos. It’s hard to get rid of data silos. When these can’t be integrated with the DMP, it may be difficult for organizations to deliver the complete customer profile data needed for decision-making, which can slow platform adoption.
  7. Sourcing From Multiple Vendors. Data integration, data quality, and other management workloads add more costs, and complexity when sourced from multiple vendors. This can limit further investment in the DMP if its costs exceed the business value delivered.

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To overcome these DMP bottlenecks, organizations need a scalable platform that is easy-to-use that can break down data silos. Additionally, businesses need to deliver relevant and trustworthy data. The Avalanche Cloud Data Platform provides data integration, data management, and data analytics in a single solution. This lowers risk, cost, and DMP complexity while allowing easier sharing and reuse across projects than cobbling together point solutions.

The post Top Bottlenecks to Data Management Platform Adoption appeared first on Actian.


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Author: Teresa Wingfield

Are You Accurately Assessing Data? Here are 7 Ways to Improve

Data quality is essential for delivering reliable analytics that business users and decision-makers trust. Organizations should assess their data to ensure it meets their quality standards. Data quality management (DQM) is the practice of using data to serve an organization’s purposes with flexibility and agility. An assessment can also find gaps in data, such as missing information, that need to be filled in, to improve data quality. Here are seven ways to improve data assessments:

  1. Assess completeness. Data completeness is the comprehensiveness or wholeness of a data set. It can be measured as a percentage of all required data that’s currently available in the data set. It’s important to note that non-essential information can be missing without making the data incomplete. For example, data that does not have a customer’s phone number will probably not impact email campaigns. Likewise, performing analytics on sales data within a certain time period will not be affected by missing information outside of those specified dates. However, for data to be complete, it must include values for all of the fields needed for the intended analytics.
  2. Ensure consistency. Data should be the same across all uses and applications. This means that no matter where data is stored or used—on-premises, clouds, apps, or databases—it must be consistent. For example, customer data in the data warehouse needs to be the same as the customer data in a customer relationship management (CRM) system. Inconsistencies can be the result of data silos, outdated information, or information entered differently across users, such as a customer name entered with various spellings, like “John” and “Jonathan.” Testing multiple data sets helps determine consistency.
  3. Confirm timeliness. Organizations want the most accurate data available at the time it’s being used. The right data must also be easily accessible when it’s needed, including for real-time or near-real-time use. The value and accuracy of data can depreciate over time. For example, data about buying habits prior to COVID-19 may no longer be relevant. Timely data that’s current and accurate helps stakeholders make the most informed decisions, uncovers new and emerging trends, and automates processes. This is where the right data platform delivers value—it makes integrated and timely data available to everyone who needs it.
  4. Validate accuracy. Data must be correct, meaning it has the right information in all required fields, such as customer profile details or product specs. The fields can include everything from a customer’s date of birth and geographic location to sales numbers and corresponding sales dates. The data impacts business areas such as marketing, billing, and product design. Inaccurate data skews analysis, so it must be correct and complete. Data accuracy can be validated by confirming a data set against a verified or authentic source. Maintaining an effective data governance program helps ensure data accuracy.
  5. Determine integrity. Data used for analysis should meet the organization’s data quality governance standards to ensure it maintains its integrity, which is the accuracy and consistency of data over its lifecycle. Each time data is duplicated or moved, the integrity can be compromised by information getting lost or attribute relationships becoming disconnected. For example, a CRM system that loses part of a customer profile, like a mobile phone number or email address, has data with compromised integrity. Data integrity allows organizations to trace and connect data. Data quality checks help verify its integrity.
  6. Measure validity. Data must match the intended use for the data set, whether it’s for analytic insights or another purpose, and must also meet the organization’s defined rules for the data. Validated data can include information that fits into specific data types, forms, numerical ranges, or mandatory data fields, such as birth months that fall within the numbers one to 12 or zip codes that contain the correct number of digits. Data should be validated after a migration, like moving data sets from an on-premise infrastructure to the cloud. Implementing data validation rules helps ensure data meets the organization’s requirements.
  7. Evaluate uniqueness. Uniqueness helps identify instances of data duplication by determining if the same information exists multiple times within the same data set. For example, if a list of 500 customers has data for more than 500 people, then data is duplicated. Data cleansing and de-duplication processes help resolve this problem.

Ensuring Quality Data Ensures Trustworthy Data Analytics

Assessing data is increasingly important as data volumes continue to grow and data sources expand. Having established processes in place to assess and govern data helps ensure the business can trust the results of its data analytics, including advanced analytics. Data that’s current, accurate, and complete also improves time to value. If it takes an unusually long time to get analytic results from a data set, there’s probably a data quality issue. Auditing and assessing data can identify issues and determine if a data set is fit for a specific purpose, such as advanced analytics. In addition, an audit can identify when changes were made to data, such as when a customer’s address, email, or phone number was updated.

Use a Modern Cloud Data Platform to Ensure Quality Data

One way to maintain data quality across the organization is to bring all data together on a single platform where it’s governed by established processes. Data governance ensures data meets compliance and quality standards. Data profiling also helps with data quality by identifying the structure, content, and formatting of data so it can be assessed and enhanced.

Actian offers modern, easy-to-use solutions for assessing and using data. The Avalanche Cloud Data Platform makes integrated data readily available to everyone who needs it. The trusted platform provides a unified experience for ingesting, transforming, analyzing, and storing data—and ensures data is complete and compliant using data quality rules.

Related resources you may find useful:

The post Are You Accurately Assessing Data? Here are 7 Ways to Improve appeared first on Actian.


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Author: Brett Martin

Why Good Data Management Matters Now More Than Ever


In the early days of business analysis and underwriting, data was managed with simply a pen and paper and, of course, Excel spreadsheets. As technology has advanced, databases, warehouses, and data lakes have enabled information to be collected, stored, and managed electronically. But as businesses have rapidly increased the volume, velocity, and variety of data […]

The post Why Good Data Management Matters Now More Than Ever appeared first on DATAVERSITY.


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Author: Jennifer Agnes

Why Data Privacy, Data Security, and Data Protection Go Hand in Hand


Here’s an important truth: There is no data privacy without data protection. Consumers and companies place their trust in the organizations they do business with and trust that their sensitive data will be kept private. These same organizations want to protect consumer and partner data, to preserve their brand as a trustworthy partner, grow revenues, […]

The post Why Data Privacy, Data Security, and Data Protection Go Hand in Hand appeared first on DATAVERSITY.


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Author: Amit Shaked

What Will the Future of the AI and Machine Learning Industry Look Like?


AI applications catering to the mass public are moving towards larger-scale use for enterprises, as big tech companies try to get into this new technology. As the use of AI proliferates in the future, tremendous amounts of computing power from cloud service providers are needed to operate these AI applications and unleash more of their […]

The post What Will the Future of the AI and Machine Learning Industry Look Like? appeared first on DATAVERSITY.


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Author: Cory Hymel

The importance and benefits of data ownership in data governance


Data ownership is a fundamental concept within data governance that plays a crucial role in ensuring the effective management, accountability, and utilization of data assets. By establishing clear data ownership, organizations can derive numerous benefits and create a solid foundation for successful data governance. Let’s explore and understand the significance of data ownership and the […]

The post The importance and benefits of data ownership in data governance appeared first on LightsOnData.


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Author: George Firican

Data Explainability: The Counterpart to Model Explainability


Today, AI and ML are everywhere. Whether it’s everyone playing with ChatGPT (the fastest adopted app in history) or a recent proposal to add a fourth color to traffic lights to make the transition to self-driving cars safer, AI has thoroughly saturated our lives. While AI may seem more accessible than ever, the complexity of AI models has increased exponentially.  AI models fall […]

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Author: Sanjay Pichaiah