Businesses are on the verge of a big shift in their approach to information. As technology becomes more and more integrated into the world around us, mid-market companies will have access to an increasing abundance of data about customer needs, employee behaviours and business processes. Big Data is now the norm – but what’s changing is the way organizations can best use it. While many companies rely on information to assess past performance, the real opportunity lies in more advanced, predictive analytics: the use of data to understand the ‘why’ and improve performance going forward.


Today, many mid-market private companies face a twin challenge. They are swimming in data, not all of it immediately useful. At the same time, most organizations are so busy growing and maintaining profitability that they are likely missing opportunities to take advantage of that data. To be more effective, companies must be more targeted, and know in advance which questions need answering.

Some organizations believe they are already using data analytics to investigate problems and uncover performance enhancement opportunities. However, typically they are focused on using that data solely to assess or account for historical performance, through dashboards that track KPIs. And often the underlying data for such dashboards is far too aggregated to be able to yield new insights.

Uncovering insights from raw data stores can draw out relationships and correlations that may otherwise remain buried, as well as inform decisions that can enhance future performance or even change an organization’s growth trajectory. This is predictive analytics' great strength. But to get to this level of maturity, organizations must have a strategy and framework to design and deploy analytics as a core function.

Once companies understand the value of using analytics in this manner, they may find they’re not set up to do so. Perhaps data systems were built in a less structured way. Perhaps the statistical and data manipulation talent isn’t perceived to be available in-house. And many mid-market companies believe they have too many foundational process and infrastructure issues to address before they can even think about analytics. Fortunately, opportunities exist to surmount each of these challenges.

Analytics can help private businesses predict and identify certain behaviours among customers, clients, and users.


The good news is analytics is no longer just for the big guys. Technology is the great equalizer for mid-market companies, and the same powerful insights and strategic advantages that help big business compete are available and affordable to mid-market organizations.

Companies looking to take advantage of predictive analytics are often best served by starting small and staying focused. First, they should identify a part of the business where advanced analytics can provide the most bang for the buck – for example, perhaps insights can be found to make one’s best customers even more profitable. Second, they should do a proof of concept with a targeted project to demonstrate value. Finally, they should ensure the right people are running it – those employees with the math and statistical foundations as well as an understanding of the business, or an outside consulting vendor if needed. Realizing an early payoff will demonstrate the value of investment in analytics, and set the stage for thinking about the longer roadmap to build these capabilities over time.

Meanwhile, the environment is ideal for those organizations looking to bring in new talent with the right analytics skills. The field of data science has become very popular and is attracting people from diverse backgrounds: economists with statistical training, engineers, and physicists, for instance. When seeking such talent, organizations should look for both technical aptitude and the ability to translate quantitative analysis into business outcomes. A single person might not necessarily possess both these skills, which is why analytics is often a team endeavour.

If resources – both talent and infrastructure - remain a stumbling block, mid-market companies should consider different operational models to meet their needs. Plenty of options exist, including analytics-as-a-service vendors, these allow organizations to outsource the analytics and gain insights quickly without a major investment in infrastructure. Such an investment will pay for itself in short order.

Finally, organizations will do well to remember that the value is in the application to the business, not in the generation of the analytics themselves. The real promise of predictive analytics lies in the ability to translate and forecast based on new ways to use data – to generate scenarios for how the business might run differently if certain factors were changed. This will translate directly into stronger tactics and strategies.

Questions to consider

  • How can analytics help you optimize your company’s business intelligence and reporting needs? 
  • How can analytics help your company best use the growing volumes of data?
  • How can you build a team that has the capabilities to keep pace with the innovations in analytics?
  • How can you get started using analytics on something small to prove the value fast?


Tom Peters
Customer Advisory Strategic Analytics and Modelling


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