Collaboration: The Core of a Data-Driven Organization

Data is a good news/bad news situation. The good news is that companies have more information at their disposal than ever before. The bad news is that they may not know what to do with it. The first step to closing that gap is identifying the problem, which is often a lack of connection between data analysts and business users.

Corporations generate oodles of information. How much? Mind bogglingly much. A zettabyte (ZB) is a trillion gigabytes (GB). The collective sum of the world’s data is expected to grow from 33ZBin 2018 to 175ZB by 2025, a compound annual growth rate (CAGR) of 61%, according to International Data Corp. 

Many corporations collect information, put fancy dashboards in front of it, and think that business process reengineering happens: voila! It doesn’t, which both the data analyst and the end user find frustrating. 

Data is complicated; many firms have tons of information at their disposal and get lost in the processes of collecting it and then digging into it. But data is only one piece in a bigger puzzle. 

To leverage it for competitive gain, data has to be quickly consolidated and include the context needed for interpretation. Data analysts and business users can then determine what the data represents and how to use the information to improve the business. But that’s also where problems arise.

The problem: Two distinct data views  

The data analyst looks at the issue from a data lens. They are comfortable dabbling with Structured Query Language (SQL) but may get lost in business jargon. When a request arrives, they take it literally and focus on extending the range of data sources, tweaking the user interface, or increasing the fields on the screen. Those are helpful steps but may not result in business-altering insights.

Compounding the problem, analysts, like most employees nowadays, are pulled in many directions and rush from request to request. They worry about letting business users run wild with data. After all, data analysts do not have the bandwidth to produce all the reports or to check every conclusion drawn from the data and their work backlog is always growing, but they’re often held responsible for how data is used, not just how it’s obtained.

On the other side, users think data integration is simple. They expect information to be available in the snap of a finger, even though they may not clearly articulate to the data analyst what they want or why they need it. They tinker with information but do not fully comprehend what they see, and quickly draw conclusions, sometimes to an organization’s company’s detriment.

The solution: Add context to data  

How does a company fix the communication problem? Data analysts and business users need to get into sync. They must have a strong toolkit that delivers information in a consistent, context-rich manner, so they understand and agree about where data came from and what it represents. Only then can they correlate it and bring about change that positively impacts the enterprise.

Here are some of the key concepts that can help data analysts and business users find common ground:

Defining a data model 

They might start off by talking about data models and their importance. Data models conceptually represent data objects and illustrate all of the associations among them. With them, Data Analysts build visual representations of data and use them to create, enhance, and enforce business rules.

By helping business users understand key data models, analysts benefit by illustrating the sheer complexity of the data work they do. On the flip side, when business users understand data models, they’re better able to help analysts troubleshoot report and dashboard creation, for example by distinguishing between a good source of data and a similarly-named dimension that’s less accurate. 

Ultimately, collaborating around a data model can create greater appreciation on both sides as analysts better understand business users’ needs and as business users start to grasp just how complex their data really is.

Why Data Lineage Matters

Data is constantly massaged as companies update their applications and create new data sources. Data lineage tracks data origins and how they change over time. 

Such records are important because departments use data in different ways at various times. The marketing department focuses on customers’ demographic information as it plans its next lead generation event while sales is interested in their purchase history as they try to close deals to meet their monthly quota. 

Regardless of how they use that demographic data, its lineage is crucial for business users. A change in source could have serious consequences for regular reporting, resulting in serious upset if an unexpected shift suddenly appears.

Understanding Proxy Data

While companies collect more information than ever before, they usually do not have complete accounts of every employee, partner, or consumer interaction. Because tracking every event is impractical, they often rely on proxy measurements, or indicators that illustrate certain patterns of behavior. With proxy data, analytics moves from science, a strict reliance on concrete numbers, to art, an interpretation of select measurements. 

Making such interpretations is necessary but can be dangerous. In some cases, business users correlate information that is not related. In other instances, they draw the wrong conclusion. Such problems can result in ineffective business processes, a loss of income, and higher expenses. 

Consequently, organizations need to put checks in place to ensure that their interpretations are correct. If not, they need to be ready and able to make necessary adjustments. By working together, data analysts and business users can ensure that everyone understands the potential as well as the limitations of even the best data analysis. 

Turning data into actionable information 

Companies have oodles of information at their fingertips. They can use it to create dramatic business change but seldom take full advantage of their opportunities. Why? Data complexity. Data volumes are vast. As it is consolidated, context is often lost. As a result, business users lack the context needed to make informed decisions and may lead the company down the wrong path.

By figuring out how to manage copious volumes of data while effectively communicating key context, companies can create a more collaborative culture around data and maximize its tremendous potential. If not, their competitors will.

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