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6 Data Quality Traits

April 18, 20235 min read

6 Data Quality Traits that Put the 'Intelligent' Into 'Business Intelligence.'

Getting enterprise performance data to work for you is like… 

well, like getting people to work for you.

Let's say you're hiring for a C-level role. Whoever lands the job will be in charge of pulling important levers.

What do you do before welcoming them to your boardroom? You make sure their story and application data quality check out.

Data-Quality Standards Should be as Unforgiving

as Your HR Standards

When applying HR due diligence on potential new hires, you ask:

  • Does what they tell you to match reality? (Accuracy)

  • Are essential details missing? (Completeness)

  • Do other sources back their story? (Reliability)

  • Do their answers match your needs? (Relevance)

  • Did they show up on time? (Timeliness)

  • Have They performed consistently? (Integrity)

Do these things right; your new hire should bring robust decision-making to make a big business impact. 

It's no different with your enterprise performance data. Whether your business intelligence platform is Finworx or Retailworx (built on top of Power BI), you must vet your data against the six data-quality traits above.

1: Data Quality Is About Accuracy 

Data quality number 1 seems too obvious. 

The importance of accurate data escapes nobody. If 2+2 doesn't = 4, or your financial data doesn't add up, you're already in all sorts of strife.

It's worth cautioning that business data' accuracy' differs from data' integrity'—data can have integrity and still be inaccurate.

Let's say that in your IT assets, a vendor's name must appear in the master list of vendors also. If the rule isn't pragmatically enforced, you have a data integrity issue, even if the data is accurate.

Before any business intelligence tool you deploy can have maximum utility, your data accuracy must be established. This needs to be simple and simple if you take the right approach to data profiling.

But the 'accuracy' data quality is worth reinforcing—not least because without it, the other five data-quality traits we'll cover don't stand a chance of becoming valuable.

2: Data Quality Is About Completeness

Data completeness for business intelligence ensures that a complete data set fuels the BI you intend to gather for optimising business performance.

In other words, before you deploy a new business intelligence tool, you'll need to know you have all the data for fulfilling your business intelligence goals.

Establishing data completeness before launching a business intelligence tool strategy can be achieved through a preliminary data warehousing project—something we regularly do for international financial institutions.

If you need more clarification, speak to us about whether or not your data circumstances warrant data management to achieve data completeness in the run-up to launching business intelligence or switching to Finworx or Retailworx.

3: Data Quality Is About Reliability

Establishing ironclad data reliability is imperative for establishing a business intelligence platform to give you an accurate circumstantial picture painted with data sourced from different canvases in your IT infrastructure.

If your ERP shows you a set of purchase orders that another IT system tells you were made on a different date, which system do you believe?

Pouring unreliable data into a new business intelligence environment will create more costs and new challenges than it solves.

4: Data Quality Is About Relevance

Business intelligence capable of widening the scope for strategic decisions breaks down when data gaps exist.

When performing data management in preparation for rolling out your business intelligence platform, don't assume excess data—having too much data is equally problematic. 

Fixing the problem of data scarcity into data overabundance creates a business-intelligence environment of redundancy. 

Do you need the data you're collecting for your BI to launch? How much does it cost to collect? Did you pre-define its utility and role in your business intelligence data-to-decisions journey? 

If you can't define and prove different datasets' roles and relevance in your new BI environment, don't collect them.

5: Data Quality Is About Timeliness

Data timeliness follows the adage that 'time is money'.

The timeliness of enterprise data flowing to your BI view refers to how up-to-date the data is. Untimely business data is more than an inconvenience, and it can be expensive.

Imagine that stock order updates in your ERP for countless stores across countless locations are delayed by 24 hours. This would cause no end of uncertainty and grief for teams situated at each location.

You might also end up with panic stocks being ordered by different departments, unaware that stock orders had been taken care of centrally the day before.

Part of the role of business intelligence is obtaining information near real-time so that old information can quickly be rendered useless with certainty.

In day-to-day terms, untimely information can be incalculably damaging to organisations regarding time, money, and reputational damage.

6: Data Quality Is About Integrity

Going back to the 'hiring' scenario in the introduction, you need to trace your work history integrity when you hire new talent across other organisations they claim to have worked for.

Equally, data integrity is about ensuring your business data is traceable across other elements of your organisation. Establishing data integrity throughout its lifecycle will determine its long-term utility in every department and workflow. 

Note that good data integrity doesn't confirm good data accuracy. As we've mentioned, data can be accurate with low integrity and vice versa, though error checking is one of the steps to establishing data integrity.

Preserving data integrity means implementing processes for data backup, accessibility and permissions, input validation and other routine checks that should be in place uniformly across your entire business. 

Strong data integrity means all that has to go right all the time. Get things wrong just once, and data integrity can break down.

Need an Informed Data-Quality Health Check?

Enterpriseworx collaborates with worldwide financial and retail entities to consistently enhance their enterprise performance data utility in the long run. Whether you're in the initial stages of contemplating data management and business intelligence or reconsidering your strategy, our team can offer valuable insights that may save you significant time and expenses in the future when costly emergency data quality projects become necessary. 

Consult with EWX's experts regarding your current position on the data-to-decisions path, and they'll guide you in determining which path to take next.

Speak to EWX's team about where you are on your data-to-decisions path, and they'll help you better understand what fork in the road you'll need to take next.

Data ManagmentData AnalyticsData Standards

Marliesa Dougans

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