---
title: Looking Back in Order to Look Ahead
description: IData Insights Blog Post - data governance necessary for improved data use
image: https://blog.idatainc.com/hubfs/StockSnap_0B9JNUXH33_ManThinking_LookingBack_BP.jpg
---

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# Looking Back in Order to Look Ahead

## Looking Back in Order to Look Ahead

[January 09, 2025](https://blog.idatainc.com/archive/2025/01) By  [Aaron Walker](https://blog.idatainc.com/author/aaron-walker)  In [data](https://blog.idatainc.com/tag/data), [governance](https://blog.idatainc.com/tag/governance), [policies](https://blog.idatainc.com/tag/policies), [intelligence](https://blog.idatainc.com/tag/intelligence), [literacy](https://blog.idatainc.com/tag/literacy), [review](https://blog.idatainc.com/tag/review)

![StockSnap_0B9JNUXH33_ManThinking_LookingBack_BP](https://blog.idatainc.com/hs-fs/hubfs/StockSnap_0B9JNUXH33_ManThinking_LookingBack_BP.jpg?width=300&height=200&name=StockSnap_0B9JNUXH33_ManThinking_LookingBack_BP.jpg)A new year is upon us, and for many organizations that presents an opportunity for reflection. What went well last year, and can we build on that success in the coming year? What didn't go as well as hoped or expected, and what might we do in response? What new initiatives might make sense? By the same token, are there practices in place we might consider discontinuing?

When you ask these questions, it makes sense that you'd like to have evidence in hand to answer them. For a long time, and for many reasons, organizations had limited tools and resources available to provide this evidence. If we met sales or revenue goals, then things went well. Maybe we could see that competitors in the same space seemed more profitable, or less. Now, obviously, for quite some time now we've had data and tools at our disposal that allowed us to dig deeper.

For example, rather than simply asking if we met revenue goals, now we can quickly find out if each of our products met its goal, or if some of them outperformed those goals by enough to cover up the shortcomings from other lines. This sort of disaggregation is common these days, and often at a much more granular level. It's worth pausing, at the beginning of a new year, and recognizing the progress that has been made. We really can do a lot more, with a lot less effort, than only a few years ago. 

Still, this level of analysis, to be really useful, depends on knowing much more information. For example, where did those goals come from? What did we use for benchmarks? How did we develop a forecast? At any point in the past year did we revisit either of those?

In our experience, once you start asking questions that data might help answer, you tend to end up with a lot more questions. Now, in our view, that's good! Asking more questions about data is a key signifier of data literacy among your personnel, and the willingness to ask and pursue those questions is a clue that an organization has, or is developing, a [culture of data](https://blog.idatainc.com/dg-culture-resources).

Even for organizations that recognize data as an asset, and which encourage data-enabled thinking, we still see many foundational challenges. Think about our example above. What counts as a sale? When we talk about revenue, is that gross or net? Is it based on when and how accounting recognizes revenue, or is a different methodology in play? If goals and benchmarks are based on previous years' results, are we defining and counting things the same way? When personnel changes occur, especially at the managerial level, or when new data applications are introduced, it's easy to gloss over these foundational requirements. There's a lot to be done to verify the completeness and relevance of your data and the insights you think you've gleaned from it, before you act on those supposed insights.

There is a notion out there, one that has re-emerged in the face of contemporary AI, that simply having a lot of data and putting into the analytics blender, so to speak, will generate insights. At a vastly oversimplified level, this was the promise of data mining when we learned of it a quarter century or so ago. Analyze enough data across enough dimensions and perhaps a trend emerges, or anomalies become visible, or non-spurious correlations can be discovered.  (We suspect that to some extent this was magical thinking about data, and that it went hand in hand with the idea that simply providing visualizations to decision makers was going to unlock understanding. But that's a different investigation for a different blog post.)

Ironically, many organizations are in a place now to throw a lot more data into their analytics blenders. (And to be fair, some haystacks really do contain needles! After all, someone just won over a billion-dollar lottery jackpot.) But our thought process goes more like this:

1. Many organizations lack the staff and/or expertise to perform even the most direct and basic data analysis - they barely meet the challenge of providing operational data products.
2. Many organizations cannot be confident in the quality and consistency of their data sets, especially over any serious length of time. This is why we continue to hear about data scientists and engineers spending so much of their time cleaning, assembling, and otherwise reshaping data prior to any real analysis.
3. Many organizations continue to struggle to develop a shared understanding of what data means in any given context. Moreover, they lack introspection into the variety of data sources they have, the scope and breadth of their data, and in many cases even the responsibility for maintaining that data.

Let's say one product or services line didn't meet its goals. In addition to the foundational questions posed above, there's more investigation to be done. Have you identified factors that affect performance? Are you collecting or acquiring data that quantifies or documents those factors? Who is responsible for that work? Where is that data stored? Who knows of its existence? Who has access to it?

We've suggested a number of questions you or your colleagues might ask about your organization's data, in order to make that data better understood and more useful. Our suggestions barely scratch the surface, but they do all have a certain similarity. In general, the better governed your data is, the easier it is to find, work with, understand, and act on that data.

Developing, revising, sharing, and enforcing [data policies](https://blog.idatainc.com/data-policy-resources) can generate avoidance and pushback, and even the wonkiest of us don't have unlimited patience. Documenting data is time-consuming, and it's easy to skip that step when facing a backlog of requests for and questions about data. And if those are your organization's vision for data governance, we can certainly understand why efforts in this area run out of steam or meet with only partial acceptance.

But think about the questions you might ask about how your organization is performing, and how data might provide deeper, broader, and even more complete understanding of that performance. How might improved data governance and data intelligence assist in this effort? Too often we have seen data governance decoupled from the actual practices of data management, usage, and analysis. Done right, data governance is what enables us to find the data we need quickly and easily, to analyze it confidently knowing that the data set has been properly assembled, and to share it to a wider audience that understands it better because of a consistent use of terminology and publication standards.

As we head into a new year, with new possibilities and new uncertainties, you might once again ask yourselves what you want to accomplish with your data, what has prevented you from reaching those accomplishments in the past, and what you might change going forward. Perhaps we can partner with you in the year(s) to come to help on your data journey.

Hope you found this blog post beneficial.  To access other resources (blog posts, videos, and recorded webinars) about data governance feel free to check out our [data governance resources page](http://www.datacookbook.com/dg).

IData has a solution, the [Data Cookbook](http://www.datacookbook.com/tour/?__hstc=129818451.4cbced196dfbfe2941653b4be756cacc.1683659469803.1699466615341.1699491634016.164&__hssc=129818451.1.1699491634016&__hsfp=2386129509), that can aid the employees and the organization in its data governance and data intelligence efforts including data quality. 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.

 [Contact Us](https://blog.idatainc.com/cs/c/?cta_guid=9863c212-c006-40b9-aba9-73db8488d49c&signature=AAH58kGIigricN3DBBtEQDkpe4EdttLlfg&pageId=140831315466&placement_guid=6765be6f-f024-407e-aadb-a1418a68ab0f&click=246e2135-1c92-4136-9158-b9409b10b325&hsutk=4cbced196dfbfe2941653b4be756cacc&canon=https%3A%2F%2Fblog.idatainc.com%2Fmeeting-place-of-dg-cybersecurity&portal_id=4171032&redirect_url=APefjpEIoLQ4UF1VlC1XeMjsdm5N2V_3gy3xJ8M14YnCSs3dyH3D1mILt3Oeqfr8k-B-mow7q2yTHgXIgqveTFDRPCHDv_Dg7gDVohWhiCNEEUz3iNPo6U6SX88_u1GWJqzRDPu-8tqPpUqM8ql1jEh8-cJ2S3651jJu5hPa8zMPyXawu0YAYx-4B6TCmhddf6H6hTzGRvMD5wPlue2UJ3OIO4_wDMXpwFCONrpq12RWFodjM5pbNmQ&ts=1699491633100&__hstc=129818451.4cbced196dfbfe2941653b4be756cacc.1683659469803.1699466615341.1699491634016.164&__hssc=129818451.1.1699491634016&__hsfp=2386129509&contentType=blog-post)

(Image Credit: StockSnap\_0B9JNUXH33\_ManThinking\_LookingBack\_BP #B1280)

![Aaron Walker](https://blog.idatainc.com/hubfs/aaron_2.jpg)

##### About the Author

Aaron joined IData in 2014 after over 20 years in higher education, including more than 15 years providing analytics and decision support services. Aaron’s role at IData includes establishing data governance, training data stewards, and improving business intelligence solutions.

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