What Clean Data Means to Your Business Intelligence and Analytics

Visualizing data is hardly a new tactic for understanding and responding to trends that impact business performance. But competitive markets put new demands on the ways in which business leaders need to derive meaning from growing pools of data.

Increasingly, businesses look to custom solutions or tools like Power BI and Tableau to bring complex data sets to life. Dynamic visualizations shed light onto discrete aspects of the business, allowing decision-makers to take their insights further and develop deeper intuitions about products and end users.

With such sophisticated tools, there is the temptation to focus solely on what they can deliver — better business intelligence and analytics. But what’s needed to deliver these things — clean data — is often an afterthought and a company’s first mistake in pursuing optimal business intelligence and analytics.

Balance Your Approach to Business Intelligence and Analytics

Distinct business goals informed by equally distinct data can and should drive one another.

When gathering, cleaning, organizing, and presenting the data behind business intelligence and analytics, teams should have clear guidelines and goals in mind as they proceed.

• What do I want to learn more about?
• What problem am I trying to solve?
• What data is needed to begin to address that problem?
• Is the way the data sets are organized conducive to answering that question?”
• How will success or learning be measured and recorded?

But when working to improve their business intelligence with better data, companies often fail to consider whether the ROI will make a meaningful impact on their bottom line.

Data Scientists Are Expensive & Not Always Necessary

Too often, business leaders assume that better BI means involving data scientists. While these experts certainly can optimize business intelligence and analytics, their role is best introduced further down the road for large companies with mature, scrubbed data sets.

Additionally, data scientists are not always versed in development best practices. This makes it difficult for them to ensure that the metrics they produce from their algorithms are both accurate and add business value.

Most companies can save money and optimize their existing data pools by doubling down on refinement efforts. Partnering with experts who are versed in balancing development best practices with strategic vision prepares data that supports and enables future business initiatives.

Data Visualization Tools Can Become a Dangerous Crutch

Alternatively, strategists may put too much faith in the capabilities in business intelligence and analytics offered by tools like Power BI and Tableau.

As effective as these tools are, simply adding them to the tech stack is not enough to reposition teams for a more strategic approach to success. Striving for better business intelligence without distinct goals is as much a pitfall as developing blindly.

Why Business Intelligence and Analytics are Useless Without Quality Data

Through queries, scorecards, dashboards, and other reporting interfaces, tools that visualize business intelligence and analytics help companies to understand how their products or services interact with users.

They paint a fuller picture that provides answers to the following questions:

• Who used the product/service?
• What pathways lead users to the product/service?
• When do most conversions occur?
• How many people engage with the product/service?

Businesses should match data quality to the size of their goals. The loftier their goals, the better the data needs to be.

What do successful teams keep top of mind when building their abilities to leverage BI?

• High quality data
• Effective aggregation of the right data
• Data management system for long term

When these elements are accounted for, accurate data visualization can begin to inform business intelligence and analytics.

Siloed Teams Don’t Produce Better Business Intelligence and Analytics

Business leaders and their technical teams cannot be isolated from one another in their quest to amass data that informs better business intelligence and analytics. Their respective knowledge sets are essential to informing better methods for gathering, refining, and presenting data.

By working together to analyze how metrics are gathered and presented in reporting software, combined teams are positioned to identify discrepancies in data quality or reporting methods that, if left uncorrected, could cause analysts to draw incorrect conclusions.

Furthermore, collaboration helps them to update data reporting or gathering processes. Data standards should cohere with current business practices and needs.

For example, by understanding data organization and trends in tandem, they can identify business decisions that may have been correct a few years ago, yet no longer suit evolving needs.

5 Questions to Ask for Cleaner Data & Better BI

Now more than ever, data is sourced from increasingly complex end points. Each company might have its own unique array of sensors, each with their own particular contexts and implications.

As such, there is no stock set of tools to gather and manage data. Combining internal and external data is an art more than a science, and each organization must proceed carefully.

Businesses approaching new ways to leverage data visualization for better business intelligence and analytics can ensure more meaningful outcomes by asking these questions about their data:

• How hard is it to extract the required data? This can depend on the breadth and complexity of the organization’s technological infrastructure.
• How will the data be gathered? Can existing personnel manage downloading and entering data manually, or will you need to implement an API to streamline this process as a growing business produces more data?.
• What sources provide the most accurate or reliable data? Does one organization provide more accurate stock prices than another? Does a source provide a larger number of meaningful data elements than another?
• How will clean data be managed indefinitely? Is the system resilient when it encounters unclean data? How are people validating quality of data coming through over time?
• What is your source of truth for your data? If data is coming from both internal and external sources, or two different on-premise databases, having some guidelines to distinguish the different assets allows for better data processing and visualization later on.

“The more data you have, the better.” It’s an unfortunate misconception, for data is useless without a shared understanding of context between business and technical teams.

For example, a chain store might use different SKUs for the same products in different locations. To the outsider, a table with all of the SKUs gives a different impression of the number of products all the stores combined sell, whereas the insider and/or business leader knows that there is some redundancy until the duplicate products are accounted for.

Teams that begin their efforts under the presumption that their data is not clean can later achieve greater ROI from tools like Power BI and Tableau. With this mindset, they can avoid the headaches such as the above example and many more. Investment in business intelligence and analytics requires first a dedication to cultivating the highest quality data possible.

Why Your Digital Transformation Will Fail Without Clean Data

The promise of a digital transformation prompts many companies to pursue better methods of gathering and presenting business intelligence and analytics.

In another era, a corporation’s review of it’s “latest” numbers took place weeks after the fact. Now that strategies shift from quarter to quarter, such intervals are no longer viable for businesses wishing to boost their competitiveness. Executives need faster answers to burning questions that Google alone can’t answer. They can’t do without tools that facilitate cutting edge of business intelligence.

The faster questions receive answers, the faster technical and business teams can iterate.

But in their rush to achieve a truly digital transformation, business leaders risk putting the horse before the cart by consulting data scientists before honing the data essential to any meaningful business intelligence and analytics.

Tools like Power BI or Tableau, while incredibly useful, won’t pay for themselves as long as they are paired with poorly maintained data.

BI bleeds into machine learning and smart algorithms, but here’s a hint: with the right approach, businesses can better position themselves from enhanced insights — without involving costly data scientists.

Image Source: Unsplash, Félix Prado