Five Ways To Become More Data-Centric

dataThe ability to parse data – turning the numbers into knowledge – is a tremendous opportunity for organizations and the finance professionals who work for them.

That’s one key takeaway from a new CGMA report, From Insight to Impact: Unlocking Opportunities in Big Data. The report uses data from a CGMA survey of nearly 2,100 finance professionals, as well as from other sources, to outline the opportunities and challenges that organizations face in attempting to harness their data.

Companies are now collecting information on many fronts – including tracking the time it takes to serve a customer, weather and its effect on business, and the number of mentions on social media. How organizations apply the data to business decision-making can go a long way to determining how successful those companies are in the digital age.

The report offers five steps to creating a data-centric business:

1. Ensure you understand which new data would be relevant to your business model and competitive position.

  • Set out the business model and the intangible assets of your business. In particular, segment the main sources of income (by customers, channels or products) and the costs attributable to each (e.g., logistics, operations, promotions, etc.).
  • Identify the data needed to describe and understand the drivers of these income sources and costs.
  • Consider what you would need to understand better to improve business performance overall.

2. Assess which data initiatives are already in place within your business.

  • Check which data platforms and initiatives are already in place within your organization and which data are already captured and/or analyzed.
  • Assess the speed and degree to which you can provide driver-based forecasting or dimensional analysis of business performance; this may be in the area of business data and not require advanced analytics.
  • Explore which external sources of data are potentially available for consideration.

3. Identify potential quick wins or small-scale proof of concept projects.

  • Assemble a team of enthusiastic people from different disciplines with the appropriate skills (IT, analysis, finance, business, etc.), backed by a high-level commercial champion.
  • Working with a small sub-set of the data available, demonstrate how insights could be derived and what value these could be to the business.

4. Conduct a formal data project to develop a related strategy.

  • Set out a full-scale data project, which will collect and analyses data and apply the resulting insights.
  • Identify the technology, skills and structure required to make the strategy successful.
  • Develop a business case.

5. Build on this initiative to start developing a data culture.

  • Ensure that data is regarded as an asset of the business as a whole. There has to be a joined-up approach between departments and a companywide commitment to assure good data quality across the enterprise.
  • Question internal assumptions, with a view to making it the norm to ask for the evidence to support any views expressed. Assist with the due diligence to verify any related claims.
  • Encourage innovation on data within the business, such as testing new data sources to explore alternative insights.
  • Tolerate failure. Where evidence emerges that a previously held view was wrong, ensure that those with an emotional investment in the position do not have a disincentive to accept the new insight.
  • Remember that data are often sensitive and valuable. It is important to respect confidentiality and apply the highest standards of business ethics and governance in the way data are handled.