You may hear “data governance” and think it’s an IT or security issue that’s not your problem, but governance has far-reaching implications beyond just locking the doors to outsiders. It plays an important role in simplifying the data analysis process and ensuring the availability of high-quality, reliable data.  Combine strong data governance with your favorite BI tools, and you begin to empower decision-making though efficiency, transparency, and accessibility. 

Why should I care about data governance? 

Here is why data governance should matter to you. Maybe you’re looking for a bit more profit margin burn – what better way to throw cash out the window than to spend hours correcting the more than four different ways to represent the name of a month (across 12 months, not including misspellings) before you are able to actually look at your data by month.   

Does your organization function better flying blind through chaos? If so – heck, this “data governance” probably isn’t for you. But let’s be real – successful and scalable businesses can’t operate this way without consequences, so let’s have a heart to heart about why you should care. 

At the end of the day, you want to do more than just collect information; you want contextually relevant information about your business, business processes, and business performance – also known as “data.”  Above all you want to use your data, and data governance is the key to ensuring this.   

Data governance establishes an internal framework for how data is collected, classified, stored, and used.  A healthy data governance policy promotes and renders quality data in a repeatable and efficient manner. This is the basis of effective analytics for both your data professionals and your end-users. Without a data governance plan in place, you can think of your data regime as a child’s room. Ask them to find their left snow glove you bought them two years ago, and they might run right to it, proudly proclaiming “Its right here!” 

NOW, try to find it yourself and you’ll be looking for a needle in a haystack, guaranteed to find every orphaned Lego in the room – twice!  If this is how your room is, let’s talk basics of how to avoid having to scour through your bedroom to find that data needle in a haystack.  

What are some of the core components of a data regime? 

Data quality.

How “good” is your data? Is there a lot of processing and/or tribal knowledge required before you can use your data?  

One of the most common mistakes organizations make is spending too much time and money trying to understand bad data, and as previously discussed, good governance is the preventative solution. 

Having standards for collection, storage, and management of your data ensure the integrity of the data is consistent and reliable. Data classification the first step and is crucial as it provides the foundation that supports all the other benefits of governance. You wouldn’t store baseballs on a bookcase, and books in a bat bag – would you? 

Quick tip: Avoid custom text fields if possible.   

Instead: Use data-type specific fields where applicable (i.e., Date, Numeric), or Pick Lists which have pre-defined choices. 

Compliance and risk management.

If you want to give compliance manager an anxiety attack, the worst answer for a compliance question is “I don’t know.”  A proactive approach to risk management helps business entities protect sensitive information and ensure compliance with rules and regulations.  When developing a compliance and risk management plan, don’t operate in a bubble. Often these types of conversations have companywide implications, so make sure to include your IT and compliance professionals early! 

Data access and sharing.

This is something we all know much more about then we realize – we share or opt out of sharing our personal data, we manage who has access to our social media pages, and we share documents from OneDrive or Google Drive. Perhaps without even recognizing it, we practice governance over our personal data daily.   

Simply, governance sets the policies for who can access what data and under which conditions. Setting ground rules for sharing will prevent sensitive information being exposed, maintain trust across the company, and maintain trust with your customers.  A healthy governance policy around data access says, “I care about our relationship” whether it’s your customers or employees.  

Data-driven decision-making.

Now to the big question: What’s it all for?  Every component of a proper governance regime brings something to the table but put it all together and we get the proverbial “main course,” the “meat and potatoes,” and the “good stuff.” We now have that contextually relevant information about your business in a format where you can trust the data end-to-end. This brings everyone in your organization together as it pertains to understanding how the data relates to your business, and enables meaningful decisions based on data, grounded in reality. Good, forward-looking analysis with reliable data is a significant competitive advantage over your competitors who are still tripping on toys. 

Efficiency and cost savings.

Modern, well planned governance practices don’t just ensure you’re getting the right data, it reduces duplication and unnecessary redundancy. Cost savings can be realized via optimizing data storage, reducing work hours from error troubleshooting, and streamlining data processes like quality assurance and analysis work. 

Your data is ready. Your room is clean. Now what? 

Selecting a BI platform 

Modern reporting and analytics require modern tools; business intelligence (BI) software is a continuingly growing component of productivity tools required for your day-to-day operations, and you have no shortage of choices.  There can be several reasons why one business intelligence tool fits your needs better then another, but one thing is true across these tools if you are a Unanet customer. In order to capitalize on the power of these tools, you’ll need to make use of Unanet’s Data Lake offering. 

Utilizing Data Lake will open an entirely new world of custom reporting and analysis, and it won’t stop at just your enterprise resource planning (ERP) system.  Implementing a BI platform can be a game changer for many companies. Some of the benefits include the ability to pull data from your various core business systems and consolidate them into one reporting and analysis model.  (That’s right – get data from multiple systems, and report on all of them in one place!) 

For the sake of this blog post, let’s assume you have now implemented a data governance regime and have selected a platform that you are going to use for reporting, analysis, and visualization. You are ready to jump in!  

Data analysis and reporting 

Let’s take a moment to define reporting and analysis.   

Reporting is a representation of a fact about a past, current, or future state. 

Analysis is the interpretation of those facts. 

We start with a quick discussion about reporting from a healthy data governance regime. The vast majority of business users start out generating reports; they want to know how much revenue a project has brought in, what their margin is, how much backlog or pipeline exists, or any number of other performance metrics. From there, managers and leaders make decisions based on their interpretation of that information.  This is a powerful transition to data-driven decision making, as it moves leadership across the organization to making decisions based on the same information. 

Now, we take a moment to talk about analysis – a subtly different skill set is at play here, such as a broader understanding of comparative statistics, forecasting, regression modeling, Montecarlo modeling, and other methods. Think about the “can you prove it to me” concept: 

Customer X has generated higher yields with lower Loaded Labor and Equipment rates across all its subsidiaries for us in the past 3 years then customer Y has – I think we should double down on customer X.  

That is a meaty statement. Let’s see if we can prove that statement in the numbers through comparative statistics and forecasting – which may require data normalization, or may have seasonality that needs to be taken into account. This is where we begin crossing the line into analysis!  

This can all seem overwhelming, and there is truly no downside to engaging a data professional for help and guidance, but for starters, you will want to clearly define your objectives. This will help keep your team focused and efficient. Align your analysis objectives with your organization’s goals and key performance indicators. Spend time with your stakeholders and clearly identify:  

1) Who is your audience? 
2) What do they need to see? 
3) When does it need to be delivered? 
4) Where does is it need to be delivered? 
5) Why does your audience need this information? 

We could dive deep into each one of the above, but for now, suffice to say these are critical questions to understand, as they will affect everything from how your data is modeled, your data access setting, and how the data is visually represented.  

Tip: If your organization isn’t familiar with data modeling best practices, it is recommended to engage an experienced professional to help get you started; however, it won’t take long for you to be off to the races yourself. 

Effective visualization 

Data visualization is a powerful tool for communicating insights and findings derived from data analysis. Creating effective visualizations can be difficult. Have you ever seen a pie chart with 27 slices? I hope not, and don’t you dare make one! Visualizations should be simple and understandable. The point of a visualization is for the reader to interpret the data in an easy and intuitive manner.   

To keep things simple, avoid over-the-top style choices or unnecessary flair. Excessive color or busy backgrounds will only distract your reader from the purpose, which is to clearly understand the data.  

Keep in mind that your end user also needs to be engaged when viewing a report, so try to tell a story with your data. Start with the problem, build your case, and then…voila! You have a solution.  

Quick tip: Data labels and cluttered dashboards can quickly overwhelm your reader. 

Instead: Use interactive slicers and drill-down reports to show detail to those interested.  

Smart data practices involve leveraging data through analysis and visualization techniques to gain valuable insights and make informed decisions. By adopting these practices, organizations can improve decision-making, identify trends, enhance efficiency, and provide a better customer experience. Smart data governance at the onset will save you time and money every single step of the way to unlocking the true potential of your data, keeping you ahead in today's data-driven world. 

About BDO USA, PC 

BDO has the largest government contracts practice of its kind serving over 1,200 contractor clients. Its team is comprised of former agency officials, industry executives, Defense Contract Audit Agency (DCAA) and other government auditors, as well as seasoned consultants, allowing them to bring the right mix of perspectives and relationships to bear and maximize value. 

Nick Clemens has more than ten years of analysis, financial reporting and enterprise resource planning (ERP) experience. He has experience administering and implementing Unanet.  In addition to his use of Unanet, Nick has developed and managed Unanet financial models for business intelligence use cases, and has written policies and procedures for the use and maintenance of Unanet financial models. 

Bradley Griese has over 15 years of professional experience, with broad expertise in corporate tooling, enablement, process improvement, and comparative analytics. Bradley is additionally well versed with industry and advisory experience in financial reporting, resource management, transformation, and change management.