In a world where everything is changing too quickly, the evolution towards agile, interoperable data services is a welcome change. Data is now able to be delivered as a service, without the need for costly investments in data centers and the resources needed to manage them. As more companies embrace the cloud, data integration and data quality need to be a more important consideration.

As a result, organizations are focusing on delivering products and services at a faster pace, and to achieve this, operational analytics is more critical than ever. Today, organizations are reliant on using their data, along with external data, to make better decisions.

And just as the cloud alleviated the expense and expertise needed to manage infrastructure, data is also seeing accelerated value from the cloud. Data lakes and cloud data warehouses make it affordable and easy to store and use all your data. So why are companies still struggling to maximize their data potential?

It’s probably due to one of these 3 culprits:

1. You failed to alight stakeholders and create a data-driven culture. This is, by far, the primary reason why most data projects fail. In fact, according to a 2021 survey of Fortune 1000 companies “executives report that cultural challenges – not technology challenges – represent the biggest impediment to successful adoption of data initiatives and the biggest barrier to realizing business outcomes.” For any data project to succeed, there needs to be strong leadership at the top of the organization and a data culture that permeates throughout the organization.
2. Your data is – literally – everywhere. I’m sure I’m not telling you anything new, but it really can’t be overstated – your data is living in places you don’t know about. It’s in third-party systems, spreadsheets on personal devices, and in public online repositories. It’s also in legacy systems which can pose a significant challenge since these are often proprietary and not always the most cooperative when you need to retrieve data regularly. These older systems are often also considered mission-critical, so if you don’t create a data-driven culture, there may be resistance from application owners. As you put together your Rockstar team of stakeholders, this is a good time to audit the systems in use by every department. This leads me to my last point on what is limiting data insights….
3.  Your data quality sucks. While it stands to reason that your data isn’t going to be perfect, it should be as accurate and consistent as possible to drive better business decisions. At a minimum, data quality requires:
   • Discovery and Profiling. Know where your data lives and what it does. Understand the accuracy and completeness of your data and use that as a baseline. Data quality is like laundry, it never ends.
   • Standard, conformant and clean data. Once you’ve done the work to understand your data, it’s important to define what “good” looks like and create rules that maintain that definition going forward. If you have a team that is focused on this today, understanding what those rules are and why they exist is a critical component of a successful data project.
   • Deduplicated data. While no one wants to forecast revenue twice, with many databases and storage residing in the cloud, duplicate data can cause more than incorrect reports. Cloud costs can easily spiral if you’re storing and analyzing duplicate data.

Today, more organizations than ever are facing the challenge of increasing data and technological complexity, but few are seeing a significant return.  To thrive in the digital era, organizations must embrace new thinking. Infusing data obsession into the corporate DNA will allow data to start driving better decisions and better results. Check out how Actian’s DataConnect integration platform can help with your data quality goals.

The post Developing your Data DNA appeared first on Actian.