Big Data and Science: Focus on the Business and Team, Not the Data (Part 3 of 3)
Let me tell you a little secret: I always know when I am talking (and working) with a company that has successfully launched big data initiatives. There are three characteristics that these companies share:
- A C-level executive runs the “[big] data operations.”
- The Chief Data Officer (even if they are the CIO) has a heavy business/operations background.
- The data team is focused on the “business,” not the data.
Did you notice that technology and data science are not reflected in any of the characteristics? Some of you may consider this sacrilege—after all, we are operating in a world where technology (and I happily work for one of those companies) has changed the data collection, usage, and analysis game. Colleges and universities are now offering master degrees in analytics. The role of the data scientist has been pretty much deified (I refer you to Part 1 of this series). And we all need to be very worried about the “talent shortage” and our ability to recruit the “right analytical team” (I refer you to Part 2 of this series).
Yes—technology has had a tremendous impact on how much data we can collect and the ways in which we can analyze it but not everyone needs to be a senior computer programmer. Yes—we all should strive to be more mathematically inclined but not all of us need Master’s or PhD’s in statistics or analytics. Yes—some companies, based on their business models, may have a staff of data scientists but others may get along just fine without one (with the occasional analytics consultant lending a hand).
What you need in terms of technology and people depends upon what you want to do with the data you are collecting. It is as simple as that. This is not rocket science folks—this is looking at your business and asking two fundamental questions:
- What business problem am I trying to solve?
- What data do I need to solve it?
Simple questions but as we all have experienced somewhere in our “corporate” past, filled with “gotchas.” And the most critical “gotchas” are usually associated with the business, not the technology or the complicated algorithm you are developing.
Knowledge of your business, its operations, the strengths and weaknesses of your people, partners, and competitors are still the guiding principles that will determine the success or failure of any big data initiative. And that knowledge must be represented in your data team.
But before I dig deeper into the team, let’s deal with the elephant in the room—the organization that the team reports into. Some may assume that IT is the perfect place for the data team as it is responsible for data collection and management. This is where some companies run into problems—you can usually tell whether an IT organization is respected by the number of rogue applications used by various groups. If the number is high, IT is viewed as ineffective and a bottleneck to “getting things done.” If the number is low, IT is respected and viewed as a valued partner. If your IT group is viewed as more of a bottleneck, you’ve got two problems:
- The group, as it is currently staffed, is not up to the task of data collection and management.
- Any group closely associated with it will be viewed with skepticism at best.
In this case, you’ve got to clean your IT house first, regardless of where the data team reports into because data collection and management is critical to the analytics process.
So what function should the data team report into? If you’ve got a great IT group with a CIO that also happens to have lots of experience with business operations, I might have the team report into that function. But note that some of the roles within that group are not going to be traditional IT skillsets so you will have to recruit from other parts of the company and then fill any gaps with external resources. An alternative would be to have the data group report into the CFO (where the focus is on the business and metrics to support it) with a dotted line to the CIO/IT to address data collection, management, and support for analytics tools and applications. Some companies have Chief Data Officers (CDOs) which are considered C-level executives and some have CDOs (or Data Managers) that report to a C-Level executive. What’s important here is this: To be successful, any big data initiative must have the support of the executive management team. If you don’t have the support of someone on the executive management team, your big data initiative will fail. Or, as Andrew McAfee and Erik Brynjolfsson put it in Harvard Business Review’s “Big Data: The Management Revolution”:
“Companies succeed in the big data era not simply because they have more or better data, but because they have leadership teams that set clear goals, define what success looks like, and ask the right questions. Big data’s power does not erase the need for vision or human insight. On the contrary, we still must have business leaders who can spot a great opportunity, understand how a market is developing, think creatively and propose truly novel offerings, articulate a compelling vision, persuade people to embrace it and work hard to realize it, and deal effectively with customers, employees, stockholders, and other stakeholders. The successful companies of the next decade will be the ones whose leaders can do all that while changing the way their organizations make many decisions.”
As you can see, while we may all be striving to live (and work) in a data-driven world, the fundamentals of business strategy and planning still apply!
Who should be on the team? As I said in Part 1, focus first on the people you have and whether they have the skills and capabilities to fill four specific roles: data scientist, data architect, data visualizer, and data change agent. While the scientist and architect roles are focused on technology and data, the visualizer and agent roles are all about your business. In other words, make sure that members of your data team come from the business side of the house. As guest poster Marilyn Craig pointed out in Part 2, knowing what questions to ask is the most critical part of the analytics process and that involves knowledge of your business and your industry—all of which can be found within your current employee roster. Or as Vivek Ratna, a partner with Digital Learning Solutions in Irving, Texas puts it in a recent ComputerWorld article on finding the business value in big data:
“Unless we can define what value can be derived [from big data] or the business leaders can tell us what value they want to get out of it, we are just playing in the dark.”
I assume that by now you’ve got my general theme: assemble a data team that has deep roots in your business and industry and you will be playing in the light.
Now, what’s next? Well, grab a pencil (remember those?) as Matt Ariker points out in a recent article in Forbes:
“The secret to getting the most from Big Data isn’t found in huge server farms or massive parallel computing or in-memory algorithms. Instead, it’s in the almighty pencil… Tremendous insights do exist in Big Data. Companies that use it well are leaping ahead of their competitors. One of the big reasons for that, however, is that they have a very clear sense of what they want to do with all that data before they start. Which brings me back to that pencil. It’s a simple but powerful tool to evade the Big Data trap of analysis paralysis.”
As a colleague of mine is fond of saying: “Every successful project starts with a great plan. Spend time thinking and writing down what you want to accomplish, what success means, and what milestones need to be met.” When you have a minute or two, read Ariker’s article and then when you have a couple of hours, dig into TechAmerica’s report on “Demystifying Big Data: A Practical Guide to Transforming the Business of Government.” Don’t let the title fool you as this is a practical guide for any business (not just government) undertaking a big data initiative. Gil Press said it best in his review of the report in Forbes:
“The heart of the report (for me) are ten cases studies of successful implementations of big data practices in the public sector in the U.S. (including the IRS, the National Archives and Records Administration and NASA), Sweden, Denmark, and Canada. In addition to focusing first on “burning requirements,” not technology, the lessons the Commission learned from these case studies are that the journey to Big Data will be (should be?) ‘iterative and cyclical versus revolutionary,’ and that successful initiatives start small, with a pilot project, but with a broad vision that allows for smooth expansion into new applications. Sounds to me like great advice not just for the public sector but also for corporations and their CIOs looking to jump into the Big Data pool.”
All I can say is this: take Gil’s (mine too) word for it. This one is a must read which brings me to a phrase I casually threw out at the end of my Part 1 post—data jujitsu. I firmly believe that the data team should operate as a startup within an established company because when you think about it, the strategy is the same:
- Analyze the customer’s (in this case, internal stakeholders) big data needs—this is where knowledge of your business and industry comes in. Answer the question: What are we trying to accomplish here and what will success mean?
- Assess the technology, applications, and tools that can meet those needs—as I’ve stated before, Big Data 2.0 tools and apps (like ours) can help to significantly streamline development efforts.
- Build something quickly to meet those needs (and avoid feature/functionality creep that often turns a 2-month project into an 18-month one) and demonstrate “quick wins.” After all, it’s the quick wins that will get the entire company excited about the opportunities that big data can bring to the table.
- Iterate until you get it “right.” In this case, right means the answer to the question in the first bullet point.
- Expand out to encompass “other” customers’ big data needs. In this case, other refers to all the preceding bullet points.
DJ Patil’s take on Data Jujitsu is an excellent resource for how to think about implementing big data initiatives while TechAmerica’s report provides real-world examples. I highly recommend that you read them both.
You may have noticed that I haven’t (yet) addressed where the privacy function fits in. If you are collecting and using personal data, you need to have a privacy team in place to ensure that you are properly adhering to privacy regulations in the U.S. and, where applicable, abroad. Why a team? Well, in the U.S. alone there are over 30 federal statutes and 100 state regulations that “regulate” some aspect of data security and privacy. If you have an international presence, you must also understand and adhere to those countries/regions regulations. In other words, there’s a lot to deal with! In most companies, the privacy team will report into the Chief Privacy Officer (CPO) and will operate as a standalone group. This group should be reviewing all big data initiatives (among lots of other things like new and updated products which may collect and use personal data, adherence to data collection and usage policies set out in terms of service agreements, privacy policies, etc., marketing initiatives that incorporate the use of personal data, customer service programs that incorporate the use of personal data, the list goes on and on) to ensure that privacy and data security policies are not violated as well as help to navigate the ethics of specific uses of data (the law of unintended consequences as well as the “creepiness” factor).
The presence of a privacy group does not, however, let the data group off the “privacy hook.” Anyone working on big data initiatives should have a foundational understanding of the privacy and big data issues. Certainly, our book on “Privacy and Big Data” (shameless, shameless plug) is an excellent resource for an overview of the privacy landscape (and we are hard at work on an update to it). The Privacy by Design website is also another great resource as is their paper on “Privacy by Design in the Age of Big Data.” Also, the IAPP (International Association of Privacy Professionals) offers some great certification programs.
Hopefully, our 3-part series has offered up some new ways to think about your big data initiatives as well as provided a more pragmatic view of data science and the data scientist, skillsets and roles, the team, YOUR business, and where privacy fits in. As I wrote this, I was reminded of that old proverb: The more things change, the more they stay the same. Don’t get me wrong. Big data will change many things about how we do business but the catalyst is still, as it always has been, how well you understand your business.
Well, I’m off to Strata East to do a presentation on data-driven day. The topic: How Much Privacy Can We Really Expect? I hope to see you there—this is a great conference so keep an eye out for my next post on Strata insights!