On the surface — these roles are simply to collect, extract and leverage data to better equip their leaders with insights.
However, this is just one aspect of a comprehensive data strategy.
The accountability for protecting data as an asset while maximizing its value also falls on the data & analytics leader.
Also in scope for a data & analytics leader should be to:Guide businesses on the journey to being a data competitor.
Steward and advocate for data management and systems including governance, security, architecture, analytics, data science and artificial intelligence / machine learning.
Set the technical data strategy with other technology and business leaders.
Foster the entrepreneurial mindset by experimenting with products and services to learn more about improving the execution of the business for its customers with a focus on delivering results.
Evangelize to rethink the ways decisions are made with data to make the business more profitable while also taking into account the need to positively impact the lives of all stakeholders and their communities.
How do I explain what I do to my 9-year-old nephew?Over the summer I was asked to explain my job to my 9-year-old nephew.
What I said was something like — “Leading data strategy means leading a team… of geniuses …who solve problems that help people do their jobs or find new opportunities to earn money by using data… which is information from past events in the real world collected through devices like phones, computers, TVs, cameras, etc.
” He was very happy with that, or it may have just been the French fries…Coincidentally this definition helped me crystallize some ideas for a speaking engagement I was giving the next week.
Ultimately, I am here and the data is here to help people (which is why we ask permission to collect it, right?).
What drives a data strategy?Your data strategy is not the same as your technology strategy.
In fact, data strategy should start with your business strategy.
I like to cite an esteemed colleague of mine and partner at Bain, Lori Sherer, who correctly calls out the need to build the right data feedback loops and the need for winning trust to drive results.
Winning trust is demonstrated through increased adoption by people incorporating data to do their jobs.
To build on this, I adapted the concept of a data feedback loop using a framework from Lean Analytics to illustrate how it creates focus.
Data feedback loops are places we intentionally monitor our internal or external systems to provide insight on what actions yield what results.
The best data strategy will empower the right people at the right time with the right information.
The ‘test and learn’ approach helps us create a scalable and repeatable business model, whether as startup or a larger enterprise in search of a new revenue opportunity.
After aligning data strategy to business strategy, it should translate easily to a roadmap.
Basic elements of the data strategy roadmap are:Objectives and goals — understanding of the strategic value of each data element for any analytics needed by the organizationData inventory — comprehensive understanding of the attributes and access needs of data being processed and stored.
Data architecture -mapping of data to the compute resources and applications that will ultimately consume and process an organization’s dataData protection — data retention and protection plan defining how data must be maintained long term to satisfy both corporate and regulatory security complianceHow will a data strategy differ in startups vs.
mature businesses?Entrepreneurial ventures look for opportunities across the landscape to provide customers with exciting new products and services.
Here is a generic example of the landscape of opportunities I saw when making a career move from technology to entertainment.
It represents potential wish list for any customer in the field.
Taking this into account, one could allocate a limited supply of our ingenious data resources any number of ways.
Illustrative exercise — what might be exciting to your key stakeholdersSo let’s simplify.
I used a framework from Lean Analytics outlining four ways organizations seek to leverage their data assets:1.
Check to confirm our facts and assumptions2.
Inform us with existing data collected directly to answer our basic questions3.
Test our existing hypotheses by translating intuition into evidence4.
Explore existing or new data sets to help us find new business opportunitiesStartups tend to start #4 while mature businesses tend to focus on #1 and #2 above.
The focus on exploration by a startup naturally positions them for a more offensive strategy.
This adaptation — as highlighted by Tom Davenport in the September 2017 HBR Webinar “What’s Your Data Strategy — illustrates a core challenge for mature businesses.
Illustrative example — a framework to review your data strategyLarger enterprises must manage their legacy technology systems in addition to varying levels of data maturity internally and externally across the landscape of suppliers, partners and customers.
Davenport recommends checking in once or twice a year on the actual allocation of resources vs.
the goals of the strategy to see where expectations meet with actual results.
Finally, a word this week on data leadership: leveraging data to innovate is driven by decisions on what data to acquire and how to use it by individual talented experts.
As volume and complexity of organizational decision-making scales, by necessity they often happen outside of leadership’s direct line-of-sight.
It is the data & analytics leader’s role to create a set of cultural and procedural guardrails for these decisions, reinforced by periodic deep dive discussions such as product demos, code reviews or security audits.
So, in summary, if building a comprehensive data strategy for your organization is a goal for 2019 — the key is focus on the needs of your business and your customer.
Also, because the field is so rapidly evolving it’s good to know that your data strategy is not something that has to be executed alone or solved for in isolation.
Whether a startup or enterprise, it can also be helpful to build an advisory panel to advise on how to leverage data to innovate and include key technology partners who are also focused on delivering innovative solutions… and it doesn’t hurt if they also like french fries (according to my nephew).