Winning with Data Analytics


It’s no secret that leading firms such as Walmart, American Express, Capital One, Amazon and CarMax use cutting-edge Data Analytics to outflank competition, improve marketing & operational efficiencies and get closer to their customer’s needs.  Making sense of internally generated data – it’s collection, synthesis and reporting – and turning it into learnings and actionable strategy is what DA is all about.  All medium to large size companies generate reams of data on their customer habits, supply chain execution and financial performance.  Yet, few of them derive as much value out of this vital asset as they could.  What separates the pace setters from the laggards is the organizational environment underpinning the DA function, specifically, the level of management commitment, cultural readiness, and analytics & IT expertise.

A recent global survey (the second one in as many years) of 4,500 business executives by MIT’s Sloan Management Review explored key barriers and success drivers around DA. The results dovetail closely with our consulting experience in a number of data-intensive Canadian organizations.  Below are some of the key findings and implications:

Analytics is growing in strategic importance

Increasingly, managers see analytics in strategic terms – outflanking competition, transforming customer relationships, sparking operational innovation – and not just a means of incrementally improving business performance.  According to the survey, 58% of respondents viewed DA as a source of competitive advantage, up from 37% in the previous survey.

Not surprisingly, the study found that “experienced” firms are extracting significantly more benefit from DA than “basic” users.  The most experienced DA companies (who utilize tools like data visualization, advanced modelling and sophisticated data mining) reported a 50% year-over-year improvement in competitiveness.  Conversely,  organizations that are employing basic functionality such as spreadsheet-based budgeting and forecasting cited a 5% year-over-year decline in competitive advantage. What are the lagging companies missing?

Leveraging analytics requires a trio of competencies

Many would suggest that deploying high performance hardware and software solutions is the best way to enhance DA capabilities and deliver a strong return on investment.  Though resources and technology are important, the respondents – particularly the experienced users – reported that demonstrated competencies in 3 areas is more crucial:

  1. Managing information, in areas such as integrating data silos, making data usable, deploying collaboration tools;
  2. Maintaining analytics expertise, around using predictive analytics, supporting scenario development, automating algorithms etc;
  3. Fostering a data-oriented culture. 

The findings and our research confirms that there is no “typical” roadmap as to which competency is more important or should come first.  They are all prerequisites.   

Like many good things, there is a risk of over-indulging in DA before a company can fully digest its capabilities. For example, the sheer amount of data can slow down decision making by creating “analysis paralysis” as well as lead to significant data management and hardware/software costs.  Leaders must set yearly DA priorities while ensuring their functional groups/divisions align to the data that directly impacts key metrics – versus what is merely “nice to have” information.

Data-oriented cultures have unique attributes

Analytics-focused companies go beyond clichés to incorporate specific cultural norms and practices that leverage analytics capabilities and learnings.  A significant majority of respondents reported that data-oriented cultures had the following key elements:

  • View analytics as a core enabler of business strategy and day-to-day activities;
  • Senior leaders and middle manager champions regularly support DA across the organization;
  • An emphasis on communicating data and insights vertically and horizontally, especially to the front-line employees who need them on a daily basis;

The more experienced a firm is with DA, the greater is their ability to overcome internal challenges around sharing information, sustaining focus and coping with poor processes.  Only 30% of experienced DA users considered organizational issues to be difficult to resolve compared with 60% of basic users.

Resources still matter

When it comes to enabling sophisticated analytical modelling, data visualization and knowledge management there is no free lunch. Companies still need the right methodologies and a robust, enterprise-wide IT infrastructure to effectively collect, process, report and manage the data.  Furthermore, there must be sufficient analytics expertise and tools at both the manager and specialist levels to effectively manage the data through its life cycle as well as to leverage it strategically.

These findings have significant implications for all companies seeking to gain competitive advantage through analytics.  Firstly, the more a firm leverages DA across and up/down the enterprise the more it will reap in terms of greater efficiencies, improved customer focus and enhanced performance.  Secondly, each company will define the DA path that best suits their competitive position, business requirements and available resources.  Although this article identified guiding principles, there is no ‘best practice’ template. Finally, mutually reinforcing factors such as consistent leadership, cultural receptivity, silo-busting information management systems and analytics expertise are essential to exploit the full potential of analytics.

For more information on our services and work, please visit the Quanta Consulting Inc. web site.

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2 comments so far

  1. Kyle McGuffin on

    Great post Mitch. I find this topic interesting. The corporate world seems to be lazy when so much information is out there on their customers and prospects. Many have installed analytics but are not using the tools to the full extent. A great time to gain competive edge.

    Thanks

  2. […] is high. By running a number of small tests, managers can identify resource requirements, learn by doing and build internal momentum behind quick wins.  Pilots could be structured around important […]


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