The key steps to achieving data quality over quantity

Do you have the data you need? Or are you one of the 50% of global executives and managers who report that they cannot easily get the right data needed to make decisions? Despite the ever-increasing volumes of data available and the fact that 59% of global leaders describe their decision-making process as empirical or data-driven, executives feel like they lack the data they truly need.

These findings were uncovered in an Economist Intelligence Unit report, Decisive Action, which explored how these leaders use data in the decision-making process in a survey of 170 global managers and executives. These insights point to a fundamental problem that delays and often prevents companies from realising value from vast information they collect.

This discrepancy demonstrates that your competitive advantage from data is not driven by how much data you can collect and store, but by how this data directly drives better decision-making.

> See also: The most common data quality problems holding back businesses, and how to solve them

Nearly all of the respondents in the report said that they made an extra effort to ensure that the information they used to make decisions was trustworthy. However, a never-ending quest to expand and cleanse data can paralyse an organisation’s ability to efficiently leverage it to reach actionable insights.

According to another recent study, 60% of senior executives and data scientists report that they are not very confident in their organisation’s data quality. Yet, rather than being paralysed by this, organisations can still realise significant profit benefit of making data-driven decisions.

Big data has been the dominant buzzword for years, but many companies have yet to translate their data into dollars. This delay often stems from deeply entrenched concerns about data quality and accessibility.

Further, CIOs commonly believe that top quality data could increase profitability by 15% according to Experian’s Global Data Quality Research. Thus, companies focus efforts on data IT overhauls, rather than using the reliable information that already exists to improve decisions and drive profit impact.

Major Enterprise Data Warehouse (EDW) development phases often take years longer than planned. These years come with a high cost due to the practical implementation costs and, more importantly, the opportunity cost of delaying actionable insights that can effectively guide key decisions.

These costs of delaying data-driven decisions are often not accounted for when projecting the profit benefit of 'cleaner' data or 'more data.'

Companies seeking to innovate simply can’t afford to wait for these EDW efforts to be fully mature before taking data-driven actions. An organisation’s data will never be perfect, and waiting for the right time to generate valuable insights can cost millions of dollars per year.

Instead of waiting, executives should take action to use their 'imperfect data' to make better decisions. While achieving data perfection is unlikely, the most important data – sales or margin data – is available at most organisations, regardless of the company’s level of sophistication.

Sales data, even in aggregated forms, is all that is needed to accurately answer the most important question: How do consumers respond to a particular business initiative?

For instance, a company may trial a marketing campaign featuring a new product line or promotion in a subset of its network. While there are many data sets that could be valuable when evaluating this initiative (e.g., brand awareness scores, click through data, redemption rate, etc.), the only data needed to understand if the program is a winner is sales data and the details of the specific marketing initiative.

While other factors can add colour to the understanding of why a program may have been successful, currently available data, when analysed correctly, often generates millions of pounds in value immediately.

There are a few key steps that will accelerate an organisation’s journey to actionable analytics:

  • Don’t be a perfectionist when it comes to data collection.
  • Focus on gathering only the most important data first. Incorporating more data types is helpful, but it is far too costly to wait on these additional data streams.
  • Put more data responsibility in the hands of business stakeholders. These stakeholders can identify which information will be most relevant to incorporate. The IT department will likely need to be involved, but not necessarily as the driver of the process.
  • Define your most important business questions and identify the metrics you will need to make a decision. This process will inform which data elements are truly necessary.
  • Co-locate your primary data repository and your analytics platform. This strategy will streamline the data aggregation and analytics process while increasing confidence in the quality of the data being used to make decisions.

> See also: How data quality analytics can help businesses 'follow the rabbit'

Following these steps can push an organisation towards becoming truly data-driven in its approach. Ultimately, deriving value from the data that exists now will bolster the business case for collecting more data and refining data quality in the future.

Collecting more or better data is valuable, but implementing highly profitable business initiatives is much more critical.

Sourced from Marek Polonski, Senior Vice President – Applied Predictive Technologies (APT)

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Ben Rossi

Ben was Vitesse Media's editorial director, leading content creation and editorial strategy across all Vitesse products, including its market-leading B2B and consumer magazines, websites, research and...

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