The BI Cantos: Establishing Data Culture

Data

The BI Cantos: Establishing Data Culture

Establishing a business intelligence system creates the conditions for reliable data and high-quality information: disciplined maintenance, a framework of tools and ongoing training, all of which foster confidence and motivation. The Center of Enablement (CoE) provides ongoing expertise and support to encourage a healthy data culture.

The goal is to spread skills for turning data into actionable information. You are improving habits, procedures and systems. These changes must support the needs of the entire company.

Incentivizing Data Quality

There is a meme in the business intelligence market:

Data is the new oil.

Data is a commodity you can’t live without. You are reading this essay because you understand data isn’t valuable until it becomes reliable and timely information.

Data quality doesn’t come from software—you must cultivate processes that produce high-quality outputs through accurate measurement, systematic learning and quality control. W. Edward Demming brought this idea to automobile manufacturing in the 1950’s with his seminars on statistical process control.

Like in automobile assembly, data operations have workflows of nuance and complexity. Each workflow serves an important function. Managers focus (unsurprisingly) on the deliverables. Data quality is assumed, but it is not a given. It must be measured and improved systematically.

Data Is Your Most Important Commodity

Accurate, complete and timely data can drive business improvement. Inaccurate data can have the opposite effect, leading to suboptimal decision-making, bickering and debate. Building an analytical business information system turns the commodity (data) into proprietary value (information).

Have you ever been in a business meeting where your CFO and VP of Sales disagreed over some number presented? Does your team quibble over KPI metrics? Is there a consensus on what is and isn’t essential to monitor? What actions are of the highest priority? Inaccurate, untimely and fragmented information is cancerous to cohesion and performance.

Who Should Own Data Quality?

If your IT team is responsible for data quality, you are placing accountability in the wrong place. Landing this responsibility on IT staff is like sending a heart surgeon to check on your friend after a nasty breakup. That educated fellow knows a lot about fixing broken hearts, but there are certainly better people to help alleviate your friend’s pain in this case.

Rather than relying on your IT team to deliver data quality, place the accountability for data quality on the teams that generate the data. Data from your Enterprise Resource Planning (ERP) software comes from work performed and managed by functional groups. Accountability and responsibility for data quality should start within those teams.

For example, you have an internal sales and order entry process. A manager owns that process. People on that team enter the orders. If you value accurate data, indicate that priority by allocating a portion of incentive pay to reward data quality improvement.

Data quality can be measured. Functional managers control the process. Producing accurate data should not be the most crucial attribute of the incentive plan. Still, it should be a significant part of manager compensation. Better, faster, more detailed information is a competitive advantage—why not incentivize it? Data culture starts by making data quality a priority.

Implementing an Incentive System for Data Quality

Establish relevant key performance indicators (KPIs) for data quality by measuring data accuracy, completeness and timeliness.

Establish a data quality baseline using at least twelve months of history. For example, measure error rates or, more positively, accuracy rates. Your operational managers will gain insight into process capability by extracting data and calculating error/accuracy rates. Hire a quality manager with skills in statistical process control and have that person help your managers establish procedures to improve data quality.

Data quality can be like any other business process improvement initiative. Create on-time metrics for data quality as well. Establish qualitative metrics by surveying the target audience. High-quality data enable teams to perform their roles more effectively. Their processes will become more reliable because those working within them will have better information.

Well-documented workflows that are measured objectively yield high-quality processes. Performance metrics communicated in each team’s language and context produce results. Establish goals for improvement, develop KPIs, and provide daily, weekly, monthly and annual progress reports.

Data quality and process KPIs will improve efficiency and process outcome—two improvements for the price of one. People love playing games, especially ones they can win. When teams improve data quality, processes improve.

In the next post, we’ll provide tips on creating a hands-on training environment through a Center of Enablement.

More About the Author

Dan Murray

Director of Strategic Innovations
Thoughts on Tableau Conference 2024 I’ve attended every Tableau Conference since the first one in 2008, held at the Edgewood Hotel in Seattle. That conference included 150 ...
Ten Questions for ChatGPT about Tableau and Level of Detail Expressions I had some fun with ChatGPT asking it questions about cohort analysis this week. I’ll spare you the 4,000 words it created on general ...

See more from this author →

InterWorks uses cookies to allow us to better understand how the site is used. By continuing to use this site, you consent to this policy. Review Policy OK

×

Interworks GmbH
Ratinger Straße 9
40213 Düsseldorf
Germany
Geschäftsführer: Mel Stephenson

Kontaktaufnahme: markus@interworks.eu
Telefon: +49 (0)211 5408 5301

Amtsgericht Düsseldorf HRB 79752
UstldNr: DE 313 353 072

×

Love our blog? You should see our emails. Sign up for our newsletter!