Data Purity isn’t the answer to the Problem of Data Quality

Poornima Ramaswamy

We can no longer let perfection be the enemy of good, when it comes to our analytics projects. As the pace of disruption across digital marketplaces increases, every organization needs to be identifying the data that is essential to their analytics program, rather than waiting for data perfection.

Today’s businesses understand they can no longer survive without mature data analytics capabilities – this is now the foundation for driving decision making in every sector. And while data can certainly tell us a compelling story about our customers’ behaviors and the direction of industry trends, the sheer volume of data available is often overwhelming – particularly when it comes to cataloguing, analyzing and managing.

According to IDC, the amount of data created over the next three years will be more than the data created over the past 30 years. Despite this gargantuan mountain of new data being produced every day, businesses are still attempting to wrangle it into perfect data sets that can unlock the golden insights they need.

Unfortunately, data has become increasingly dynamic, complex, and unwieldy. It comes in all shapes and sizes of both structured and unstructured data – which usually means it takes a great deal of organizing and sorting before it can be used in your analytics tools.

Perfection Is Preventing Analytics Possibilities

As every business attempts to get their analytics program off the ground, they will often hit the same quality hurdles on their way to becoming a data-driven organization. While they slave over non-compatible data formats to create a centralised repository of perfectly formatted and federated data, they can get dragged into a costly exercise that could take months or years – before they ever see a single insight that offers any real ROI.

Meanwhile, the digital marketplaces they operate in and their competitors are running past them. By the time the data is finally ready for analysis, the questions they were asking have now become irrelevant. The risk then becomes going back to gut-level decision making vs. leveraging data in a more effective way, compounding the issue.

Business users need relevant answers today, and without an effective data analytics pipeline strategy, they will continue to revert to decision making as usual. Thankfully there is a clear path forward out of this cycle.

  • Forget about 100% data purity – Having a shining monolith of perfectly formatted and compatible data for every possible analytics project is a fantasy. Achieving 100% data purity isn’t just a challenge, it’s an impossibility. Once you embrace this reality, you can free yourself to adopt a more agile approach to data based on what the business really needs.
  • Focus on the business challenge to be solved – Rather than looking at the data you have and asking what challenge it can solve, focus on the challenge first. What are the top-level business KPIs that need to be met, and what is standing in the way of achieving them?
  • Find the data to meet the challenge – By determining the value and relevance of data needed to meet your objectives, you can focus on creating a representative repository of data to feed more impactful analytics efforts.
  • Use the learnings from each analytics project to drive broader success – With an agile approach, your organization can create a repeatable process for formatting and cataloguing data for the analytics projects that will actually drive value.

We can no longer let perfection be the enemy of good, when it comes to our analytics projects. As the pace of disruption and innovation across digital marketplaces increases every day, every organization needs to be asking what data is essential to your analytics program in order to consistently drive your KPIs vs. waiting for data perfection.

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Poornima Ramaswamy

Founder, Pivot X