Over three-quarters (77%) of CIOs ranked data-driven decision-making as their top priority in our CIO trends report, the highest-ranking area of focus. This should not be surprising; we are currently in a highly competitive world that is still surrounded by economic and geo-political uncertainty. This is driving a need for greater agility, but such agility is only beneficial if it is directed by better-informed decision-making.
Data is viewed as the critical component of a better decision; without it, everything is subjective and inherent to errors in judgment and natural biases. Armed with data intelligence, you can drive better and faster decision-making; it is obvious. Understanding what has happened in the past and spotting trends in the now enables you to better predict the future and make the right, timely decisions.
the data exists, we just need to harvest it
We are living in the digital age and what that means is that for most organizations the data exists and in a lot of instances, this is big data. We know how our customers interact with us, we know their purchase history, we have operational data, and we have financial data. The challenge faced is piecing together this data to provide the full picture and harvest the hidden intelligence.
According to Statista it is estimated that 120 zettabytes (120 billion terabytes) of data will be created worldwide in 2023. However, it is highly likely that the vast majority of this data will simply remain untapped in terms of the intelligence contained within it.
data has to be accurate & trusted
For data intelligence to be valuable, it has to be accurate and trusted; otherwise, you may act on misleading information. Recent surveys reveal that only a small percentage of organizations achieve an acceptable quality score across their data, and a significant number of IT decision makers lack complete trust in their data. To enhance data accuracy and build trust in the data intelligence process, a stronger focus on data management and governance is essential.
meticulous data management
Best practice data management spans the whole lifecycle of your data from tracking its origins through to assuring its quality and integrity. It defines and manages how data should be ingested, processed, stored, secured and accessed.
Data management is integral to data modeling; in order to understand your data, organizations need to know its origins and how it is managed. This enables them to not only create a single source of the truth but to build trust in its integrity.
governance is essential to underpin trust
In order to build trust in data and data management, organizations need to have in place a robust data governance framework. Not only does this need to span the complete lifecycle of data, it also needs to define how people, technology and processes interact with this data and how they manage ownership, access, security and quality.
For example, data providence is extremely important; it does not only define the origins of the data but as part of data privacy legislation, it enables organizations to demonstrate they are protecting data sovereignty.
Likewise, defining, controlling, and managing meta-data is vital in order to track data and understand its origins and relevance in creating actionable insights.
data management is a science that is under-resourced
The top reasons that organizations fail at data governance are that it is treated as a project and not a continual program. It is viewed as an IT project and not something that should span every part of the organization. There is a belief it can be solved with a tool when it needs a focus on processes and finally, organizations neither have the available resources and in many cases skills, to do this right.
Partnering with an organization that has real expertise in this area can really help; injecting the expertise and experience of best practice to help put in place your governance framework, change mindsets, and inject the specialist skills to drive and ensure rigorous data management.
It is easy to get distracted by the exciting developments in data analytic tools and the use of AI and ML to automate data processing, but unless organizations have in place the right data governance and management, you will never have the confidence in the data intelligence created.