In my last post, I:
- Made an argument that data promises to greatly improve (quality, effectiveness and efficiency) both the products that companies offer and business operations making companies that exploit what their data has to offer more competitive;
- Suggested that there are issues that inhibit a company’s ability to take advantage of this basic built-in resource; and
- Implied that not taking advantage of the data would be negligent and that the use of data will become necessary to be competitive going forward.
In this post, I suggest steps (on a macro and micro level) that we can take to improve their ability to tap into this resource. The items are listed in order of importance, in each category respectively:
- Acknowledge data analytics for what it is: 1) a mechanism to continuously improve business performance; 2) a productive strategy to extend and supplement your product offerings; and finally 3) a potential source of serious revenue. Data analytics is a triple threat and understanding its potential is the first step to exploiting it;
- Produce a unified definition of analytics. This is an ongoing process because the components that define the different approaches to analytics are constantly changing. But, without a complete and well-defined description of what analytics (both process and structure) is, it is difficult for practitioners to critically evaluate the potential tools as they work to apply the best techniques to the challenge at hand;
- Develop methodologies that are designed for both business and technical participants. A good methodology provides a shared approach (which is important for a multidisciplinary team, which is required to fully exploit the potential of analytics) that effectively guides the team through the steps necessary to consistently produce the best outcomes;
- We need to enhance data science training to include the kinds of tools that process consultants use to understand and reengineer business processes. This initiative has two targets:
- Making the data science domain experts more capable of analyzing business processes (working with business people to find the high yield opportunities) with a view towards applying their core training and experience (in data science) to the challenges at hand. This effort would focus on teaching the basics (and more advanced aspects as necessary) of business process reengineering which are key to introducing new technologies in ways that impact business processes and produce good outcomes.
- Provide data scientists the training and (more importantly) experience that will enable them to effectively collaborate with senior business leaders to strategies and tactics that support the incorporation of data analytics into the mission critical business processes and marquee products. This effort would focus on teaching advanced consulting skills. There is too much order taking and too little multidimensional (i.e. high value) consulting taking place.
My experience at PwC suggests that only 1 in 100 smart analysts will be able to master both the domain expertise (in this case data science) and the art of being able to collaborate with senior management. So, the process takes a substantial investment. It requires both training and mentoring. Many professional services firms have programs that can be the model for this kind of initiative and I would suggest that parts of it could be embedded in both college and graduate level programs, but it is clearly work that will need to be carried on long after graduation.
- Eliminate the stigma of technical subjects (especially data-related) being geeky from corporate culture. General managers (junior and senior) must be able and willing to participate in discussions that have substantial technically content. Some of my most productive collaborations with senior managers were the ones who were willing to engage in discussions of technology as being necessary components to the success of their overall business/product strategy. The most notable was with Pat Allin where we exchanged ideas and wove together solutions that addressed strategic business issues in innovative (through the use of technology) ways.
- Assign responsibility for this initiative to a senior member of your business management team. Make their responsibility (measurable goals and objectives) for the development of the initiatives associated with exploiting your data assets. This should include: bootstrapping the initiative (see following bullet points) and then ensuring that data becomes an integral part of the thought process every time a product is being developed/enhanced or someone is looking at business processes with an eye toward improving quality, effectiveness or efficiency.
- Develop a data strategy. Take the time to understand what data assets you have, where you are with regard to exploiting them and where you would like to be 1, 3 and 5 years from now. This is not a small task. Some of the data assets will almost certainly be in place and benefits of exploiting them will be obvious. Others will not be so easy to identify—and this is where the gold might be. They will require that operational systems start collecting data that is reasonably easy to cultivate, but is not currently being generated and collected.
At the end of the exercise (at a high level), you have an prioritized inventory of the assets you are going to develop, descriptions of what they are and how they will work as products or business process improvement opportunities, the resources you are going to need to execute on the strategy, the timeline for implementation and the ROI for each investment.
Make sure that senior management is involved in this work and understands the opportunity (to a reasonable level of detail) and buys off on the plan.
- Develop a strategy to attract, develop and retain top notch data scientists. The data game is just beginning. It will be big and it is a long-term play. If you are a smaller company, you might consider outsourcing this function to a partner (which means developing a relationship) who has the expertise and resources to work with you on your data-related initiatives.
- Bring together a multidisciplinary team (whose members have a defined time commitment to the effort) that can concentrate on data related initiatives, whether they are product development related or focused on improving the company’s operational ability to deliver high quality goods and services efficiently and effectively.
In closing I will state categorically (no ifs and or buts) that companies that miss the data analytics boat will be the losers. The winners will be those who embrace data and analytics as the powerful tools they are, embracing the data science disciplines necessary to succeed in the 21st century.