Unlocking the data barriers that prevent your analytics team
The pressure on analysis teams today
You may have an analytics team, or you may just be starting to build one. Either way, there is increasing pressure to extract valuable insights from your data. This is for good reason, as an impactful data and analytics program can lead to incremental revenue, reduce costs, and increase efficiency. All of this can be a differentiator in the competitive market you play in.
You know you have valuable data, you have talented analysts, or you bring new talent into analytics. So valuable information should be ready to flow in short order, right? It doesn’t happen that fast!
Challenging data accessibility
The reality is that no matter how big your ambitions are to use analytics at your organization, your analytics team probably doesn’t have all the data they need to fully realize them. It’s possible that the data they need will be made available to them over time, but the time required will vary greatly depending on your existing data architecture and resource availability.
Unfortunately, the truth is that team members with the technical skills to transform data to make it usable can be overextended. As a result, the analytics team may spend most of their time disputing data in tools like Excel before ever getting to the stage where business value can be extracted. This has unfortunate consequences and often a devastating impact on the return on investment from analytics. It means that the speed at which value can be extracted from analytics is slowed down, lowering the overall return on your analytics function because team members spend only a fraction of the time finding information.
Opportunity to supercharge your analytics team's return on investment
Improving your analytics team’s access to the data they need will increase:
- the volume of value-added information they provide
- the speed with which it is delivered
This will in turn lead to short and long-term ROI gains from the total investment in analytics at your organization. Think of it in terms of the following example:
Imagine a well-staffed production facility with modern equipment. However, it produces only 20% of the products it would be capable of producing. This is because it cannot access the raw materials needed to produce the other 80% of the products. How disappointed the investors in this production unit must be with its poor return on investment!
A manufacturing plant without raw materials cannot produce products, just as an analytical team cannot generate results without data!
The same phenomenon can unfortunately happen with analysis teams! They face the challenge of not being able to produce a wide range of projects that would bring maximum value from analytics, because they have to track down the necessary data and make it usable. This would be comparable to people working in a manufacturing plant having to find and organize the raw materials needed for production themselves!
Fortunately, this problem need not exist in the analytical world. A semantic layer managed by those using data to extract insights can bring the team closer to its full analytics “production” capabilities!
How it works
Effective semantic layer management puts the management of data sources in the hands of those closest to the information. Need access to a previously unavailable data source? Or perhaps you want to combine two data sources and create a business calculation that is available to all users? With a semantic layer, everything can be just a few clicks and keystrokes away from reality. Without it, it could take many months, force potentially challenging conversations, and put additional stress on valued employees on the path to making it accessible.
ROI across the organisation
Perhaps the best part of this is that the benefits of a semantic layer extend beyond your analytics function. They reach deep into the organization, with talented non-analytical teams, from product managers to sales reps, having improved access to the data needed to quickly answer important questions.
In addition to increasing the overall ROI from analytics, a semantic layer also increases the likelihood that important information can be discovered when it is most needed. Sometimes a data point is more valuable today than it will be a month from now. The semantic layer can open up new opportunities, ensuring that the data you need today is available today. This opens the door to recognizing benefits from your data that would never have existed without it.
Let’s look at the impact that modern data preparation tools can have in the context of a simple example that occurs regularly in many organizations. Note that business professionals or data analysts (non-data engineers) can use these tools to make short-term improvements to data dispute challenges.
The analyst who always provides the most important reports is behind again. Business leaders really need to use the data from these reports, but getting that data depends on one overwhelmed person. This isn’t the first time it’s happened either; reports are usually late. The main reason this analyst gives for this is that there are a lot of steps to gather the data.
Problems with this scenario
The problems are many. One important thing is that the organization has a key employee (the analyst) who works long and unpleasant hours, gathering reports that are already overdue. Another is that the business stops making important, timely decisions in the absence of the information these reports hold. There is also an internal blame game as to why it is “so hard to get the data”. This is inefficient and costly.
What needs to be done (long term)
In the long term, the organization needs a holistic data strategy that enables the delivery and availability of quality data to the right people in the business. While this should be an aspirational goal, it is not always achievable in the short term. Fortunately, there are options that can make a dramatic impact even in a matter of weeks.
What could be done now to help essentially (in the short term)
There are many tools available on the market today that offer such short-term support. Such tools allow “data transformations” to be done by non-technical users with a desire to learn them. They allow automation of the processes required to compile the data needed for critical reports.
NOTE: this approach will not solve the root causes of the problem described above, but rather allow for a short-term “quick-win” substitute solution. Implementing a holistic, sustainable data strategy is the right long-term approach and a completely separate topic.
Imagine this enhanced scenario
After the analyst finishes the most recent round of report creation, his manager gives him time to learn one of these tools. The analyst invests additional time in using the tool to build automations for the steps he or she needs to take in preparing data for each report. This upfront investment pays immediate dividends in the form of:
- Reduced effort on the analyst in preparing data for each round of reports;
- Increased speed of report creation (the business gets them faster);
- Workflows created in these tools can provide insight into what is needed for long-term automation.
Source article: https://dataideas.co/ideas
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