For many, many years now, part of the prevailing business discourse has been that data-driven or data-enabled companies and organizations outperform their peers and competitors. Why is that? In short, it's because these organizations use data to have a better understanding of how to improve operations, or to see more clearly into how the market is changing, or to more quickly assess the impact of experiments and the outcome of decisions. In this blog post will discuss a culture of data and some barriers to this culture.
And yet, for just as long, survey after survey has revealed that most organizations, from businesses to nonprofits to medical centers to educational institutions, do not believe they are doing enough with data. Some are not collecting the right data, others are not able to analyze data well enough to derive actionable insight, still others have leaders who are hesitant to act on data that has historically been unreliable, if not downright untrustworthy; many of our clients exhibit all three of these characteristics!
Some of our clients have identified as an organizational goal, the creation or continued development or overall strengthening of a "culture of data." That means different things to different organizations, of course, but common elements we see include recognizing data as a valuable business asset, seeking to leverage data or extract value from it, looking for ways to apply data to organizational problems or goals, and of course, incorporating data into planning and evaluation processes.
One barrier to a culture of data is having a workforce that is equipped to handle data at the levels of complexity needed in modern enterprises. We have discussed data literacy in this space before, and it is just one of several data-related competencies you'd like to see in your knowledge workers. These employees need to know what data is collected by the organization, whether it is relevant to their work, where to find it, and who is responsible for it, at a baseline, really.
Another barrier can be found in technology, or data architecture and infrastructure. We at IData are basically agnostic about whether specific tools, applications, and platforms are better than others, but we have a pretty firm belief that the modern data ecosystem requires strong performance in data collection and processing, data integration and curation, and of course, BI and analytics. It is far too common for us to encounter organizations that have decided to upgrade or modernize one or two tools, but which haven't stepped back to look at the entire data life cycle. Data visualization tools are pretty much a necessity for modern organizations, but without curated and reliable data sources these tools can actually make it harder for users to understand and trust data. Niche software-as-a-service applications offer intuitive user interfaces and robust performance, but if not managed carefully they can make data silos even worse.
A third barrier can be the data itself. The big question is, is it suitable to help your organization succeed? A large component of suitability is the quality of your data, and we will come back to that topic in more detail in a few weeks. Is the data current enough, and is that current subset sufficiently complete? Is it relevant? You might say, well, we use it for our management or operating metrics, so of course it is relevant. Well, sometimes we measure what is easy to measure, not what is important. What do you have to do to/with your data to get it into a position where it can be mined or queried according to key business or strategic concerns? Transactional systems are typically not organized in ways that lend themselves to analysis, and traditional star or schema reporting stores were optimized more for speed and convenience than for analytics.
In order to decide on the suitability of your organization's data, you probably need to gather up a lot of intelligence about that data. Where did it come from, and when and how; where is it stored, and how often is it updated, and where does it go (if anywhere) for further use or enrichment or study; how broad is it, and how deep, and how complete?
All of this, then, is part of the work of data governance and data intelligence: identifying or cataloging data assets; understanding the parameters of the data you've collected or are collecting; classifying data according to regulatory or business requirements; profiling data for completeness and consistency as a prelude for future enhancement.
Now, we are not saying, do not run before you walk. Significant insights can be gleaned from small data sets, and you should always be looking to extract value from your data. But, without the guardrails and guidelines that come with data governance, that work will be harder to perform, it will be more difficult to replicate, people will be less likely to trust the outputs, making it more challenging to act on or even share.
The good news is, gathering intelligence about your data assets does not have to be invasive or expensive, and it could have immediate tangible benefits, such as reducing waste, or answering auditors' questions quickly, even before you set yourself up for analytics success and a vibrant culture of data. We hope you enjoyed this blog post.
IData has a solution, the Data Cookbook, that can aid the employees and the organization in its data governance and data intelligence efforts. IData also has experts that can assist with data governance, reporting, integration and other technology services on an as needed basis. Feel free to contact us and let us know how we can assist.
(Image Credit: StockSnap_AD76394B17_LockonGate_KnowingDataAboutData_BP #B1271)
