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The Power of Data Integrity and Discipline

Posted by Sean Jackson, Director of Strategic Pricing & Business Analytics

In today’s data-driven organizations, it is increasingly important that data governance be business led and discipline focused. 

small_big

The equation is simple: to draw meaningful, actionable conclusions from your transactional database, you need to sweat the small stuff early, often, and consistently. It is critically important that each discipline in the business vet and ultimately align on data labels for effective segmentation. If you don’t, analytical exercises may not yield the big data results required to supplement your already-sound business practices. Data classifications need to be structured to fill the various needs from the Sourcing, Sales, Procurement, and Quality departments.

discipline

Each department must understand and accept their responsibility in maintaining data integrity. To get there, you need to start small. It begins at the part-build level. Each department will need to help build the part number to allow you to go deep and wide with summary data builds. For example:

  •          Sourcing for commodity classifications and costing profile
  •          Quality for inspection requirements and material/finish designation
  •          Sales for sell price and E&O categorization
  •          Materials for inventory policy

Do not allow your fields to be freeform. Instead, create dropdown lists for all items in the category and control changes at the list level to maintain consistency. Any weaknesses in the entry process may cause incoherent results. Poor data integrity can also cause many problems. Some of which are:

  •          People can’t get the results they are looking for. Think garbage-in, garbage-out.
  •          Inaccurate reporting breaks down trust in the system.
  •          Frustration leads to lack of use.
  •          Reduced ROI on database development costs.

Articulate your data discipline expectations and require all departments to support them and comply.

higherarchy

While written queries work well for timing and exception analysis, working toward meaningful hierarchies is the key to successful, big data results. Waterfall tiers are a great way to logically allow flexibility your business will need to stratify your part or customer base. Start at the highest level and build to the lowest.  For raw material, it may be something like:

Capture

This will allow your team to pull data at various levels needed to quickly segment customers, products, vendors, materials, etc. Hierarchies should be tight and right, so spend the time upfront.

output

A well-constructed database will allow segmentation across formerly independent elements. ‘What if…” questions abound in the data:

  • ‘What if…” understanding what commodities you are not selling to certain customers in peer groups may help your sales team know what to ask about on their next sales calls.
  • ‘What if…’ your sourcing team needs to understand how manufacturing processes line up with various suppliers’ capabilities in the supply base for the specific materials required.

Dream BIG but start small to craft the efficient, effective database from which your Business Analysts can draw. Your solution experts no longer reside in IT but reside much closer to the business hub, so arm them with the tools necessary to augment your business intelligence and practices. Big data discipline will not solve all your business challenges, but it will help prioritize and define opportunities to ensure future success.

Topics: Data Discipline, Data Integrity,, Data Governance