This article was first published on February 26, 2017 on my linkedin page: https://www.linkedin.com/pulse/five-discomforts-business-analyticsunderstand-them-seamless-dapaa/
One major headache of almost all chief analytics officers is the slow adoption of analytics and evidence-based culture by the business departments of their respective organizations. And just as a commercial product is justified by its acceptance in the market, the investments in analytics are redeemed by their adoption into business decisions and operations. Hence, the head of an analytics organization cannot take the enthusiasm of his business partners in analytics and solutions woven from it for granted. This is why it’s also salient for analytics organizations to understand, in profound detail and nuance, the pain points of the business about analytics solutions, journey and culture. That way, they will not only be able to manage them to the barest minimum, but also navigate a smooth flight of their organizations up the rungs of the analytical maturity ladder. For when it comes to the selling of analytics, we can all learn from Sam Walton: The business partner—the consumer—is king! This article enumerates the five major anxieties I have observed in my long career of promoting and propagating analytics in various organizations, and discuss effective ways they can be eliminated if not alleviated. All or most of them should be familiar to readers who have tried to deploy analytics in their respective organizations. But even if familiar, the reader will benefit from either the reinforcement or the alternative perspectives the article provides, and will hence be better equipped to deal with such issues when confronted.
Transparency: Data Reveals My Problems to the World; Analytics Illuminates my Inefficiencies.
Mullaly, the erstwhile CEO of Ford, who rescued the iconic company from the jaws of bankruptcy a decade ago, coined one of the pithiest axioms: Data will set us free. However, despite its self-evidence, not all business leaders believe it. Unfortunately, even though almost all business leaders believe in the potency of data, few leaders find data to be liberating. Anytime you shine the light of data on the operations of any business unit, you will almost likely illuminate many insights and intelligence which are hidden in the otherwise unreachable souls of the business. Different leaders, based on their egos, will respond differently to these newly found data wisdoms. The humble leader, like Alan Mullaly, will see data as the catalyst which, when combined with business acumen, will revolutionize existing business models and create newer ones. Nevertheless, a leader, who is celebrated for the spontaneity of his gut calls, can misinterpret the power of data as a competitor to his competence. In this leader’s eyes, he, not data, should have found that golden pocket of business that’s estimated to double revenues and profits in five years; he is needlessly worried that all the newly discovered opportunities revealed by data will be regarded rather as problems he missed; and thus, he will fight the institutionalization of data and analytics with all of his inner energies. In essence, the high resolution of data’s clarity rather becomes its curse.
Helping such a business leader recover from his insecurities with data transparency demands the empathy of the analytics leader. The analytics leader can allay such fears by presenting himself as a trusted partner genuinely interested in helping the business leader succeed with data. He must be willing to credit all (or majority) of the early wins enabled by data to the open-mindedness and vision of the business leader; however, as the relationship matures, the analytics leader can negotiate for future wins to be celebrated as fruits of the collaboration of both teams. Such celebrations will serve as success stories for other business leaders and enable the whole organization to see in color the beauty of analytics. To preserve and perhaps precipitate the relationship between the analytics team and the business, the analytics leader should desist from the temptation of publicizing gotcha facts uncovered by data, and rather focus on insights that truly transform the business and make its respective leader look savvy to the organization.
Obsolescence: Analytics Will Make Me Lose My Job
This is the most serious of all the discomforts a business leader can have about Analytics, and hence when encountered, should be resolved as urgently and exhaustively as possible. This is because few concerns can be as menacing as the threat of a job loss to a business leader or any employee for that matter. Unfortunately, in the eyes of many business leaders, analytics is seen not as an enabler of opportunities, but a way to reduce human resources. “If all of these golden future opportunities will be mined from data, and not my mind, then what’s my value to the organization?” the business leader ponders. He also contemplates what will happen to his team of business analysts when most of their manual routine jobs are automated.
In a world, where leaders romanticize about their business omniscience, and analysts pride themselves in how many excel sheets they personally and manually created in a day, the threat of obsolescence cannot be taken lightly. The analytics leader should find it an inexorable responsibility to articulate how the business functions will evolve into more matured and valuable roles with data analytics. For instance, the business analyst who, in the past, spent his whole day carving lists of trucks scheduled to be completed for that day, can now have this list automatically generated by some predesigned computer program, which also provides other useful information such as the probability that the scheduled trucks will be completed on due dates. With the time savings from automation, and new insights(or perhaps foresight) on the subset of trucks that may be delayed, his new role will be having discussions with the manufacturing team on the trucks whose completion are at risk—A role that’s far more exciting and valuable than the mere and manual production of excel spreadsheets of trucks. Likewise, with the right data tools and insights, the savvy business leader can augment his rich stock of business knowledge and experiences, and be better armed to come up with even more powerful strategies than previously. In our new world of increased market competition, and pervasive threats of disruptive entries (such as Uber in the personal transportation industry, Netflix in the entertainment industry, to mention a few), this augmentation of intelligence provided by data is a compelling proposition to the business leader.
Therefore, to be a successful analytics leader, you ought not be only knowledgeable in statistical and quantitative analysis, but also, and even more paramount, have a good grasp of the workings of the respective businesses of the enterprise. This will allow you to clearly demonstrate how your analytical products rather empower than dissolve the business unit. Just as a fish needs gills and fins to thrive in the ocean, you need technical and business knowledge to thrive in the enterprise environment. Your quant skills are your gills; they allow you to survive in your role as an analytics leader. Your business savviness are your fins; they allow you to navigate the tricky business waters.
Analytics is Slow: I Need Results and Now
As the Analytics leader develops more rapport with the business and begins to gain their enthusiasm in his products, he also meets their impatience. No good deed goes unpunished indeed! Analytics bear really beautiful fruits but not without a heavy dose of time and effort. To carve out powerful and meaningful insights from data, the data team goes through a painstaking experience of understanding the elements of data needed, querying them from their respective data sources, integrating them into an analytics dataset, cleaning them and addressing any issues (such as errors, and outliers), choosing the optimal analytical methodology for the problem at hand, executing methodology, reviewing results for accuracy and meaning, and then eventually communicating results to the interested parties. Data exploration for competitive business insights can therefore be lengthy, with a strong correlation between the profundity of the business question and the gestation period of the solution. While this may feel inconvenient on the surface, it’s a great thing that the quality of insight is dependent on the quantity of investment. Nothing easily achieved can be competitively valuable. And the other remarkable boon is that, the investments are paid for by the scale of application of the insights, both across business functions and time.
Thankfully, there are number of strategies the analytics leader can leverage to effectively manage the impatience of the business leader, and make the journey feel less prolonged. I will provide three of them. The first is to structure the analytics department to align with the different time horizons of analytical projects: An analytics function should have a team that handles quick and short-term projects(durations less than a month); one that handles medium-term projects(durations within 1-6 month timeframe); and one that grapples with questions and thoughts about what the businesses ought to be (6+months timeframe). This kind of structure will allow the analytics team to be agile and responsive to the varying needs of the business while ensuring that you are addressing analytical problems of diverse complexities.
Secondly, the analytics team has to engage the business partner throughout the project’s gestation period, constantly sharing updates and interim results no matter how preliminary. The analytics team has to avoid the temptation of developing solutions in isolation, only to return to the business partner after final results are ready. Such aloofness cause the business to prematurely make decisions without analytics input and quicken their resistance to the results.
The final recommendation is for the analytics team to empower the business to develop light to medium capabilities in analytics so they can answer their quick data questions themselves. With the advent of user-friendly data science toolboxes like Watson Analytics and Tableau, citizen analysts (a term coined for business analysts capable of addressing mildly sophisticated analytics questions) can be trained and developed within the various business functions. This will help immunize the analytics department from being inundated with a large number of non-consequential analytics projects. The analytics team may find the idea of cultivating citizen analysts to be menacing but it’s the biggest blessing it can ever enjoy.
Analytics is Foreign: I Don’t Get It and They Don’t Get Me
Anyone who has sat in a joint meeting between an analytics team and a business unit is familiar with this communication breakdown. Either party talks over the other’s heads and little, if any, meaningful progress is made. This makes the journey of weaving data analytics into business decisions and the propagation of an evidence-based culture torturous and tortuous. The confusions of the business leader about analytics are understandable given that it’s a relatively new practice within most corporate arenas. Twenty years back, the CEO didn’t call on his chief analytics officer for input on how to drive incremental sales with data. He relied predominantly on his business experience, and those of his fellow executives. And in the cases where business experience failed or were not reliable, he relied on his guts. However, with the proliferation of data and analytics toolboxes, the potency and romantic appeal of the gut are quickly dissipating and becoming almost primitive (see my paper titled ‘From Gods, Guts to Science: The Golden Era of Data Analytics in Decision Making’). Most businesses are becoming so complex and nuanced that winning is almost impossible without relying on the high resolution of clarity that data provides. Nevertheless, most business leaders are hesitant to call their analytics leader because they are always rewarded with drab discussions of technical solutions they have little appreciation for. In fact, this reason has been broached for the slow rise of the analytics leader to the C-suite.
Hence, one of the non-negotiable competencies of an Analytics leader is his ability to engage the eyes, minds and hearts of the various leaders of the business units and the organization as whole. He has to do this with captivating visualizations, logical evidence-based analysis and relatable stories. He has to develop a keen sense of how the business leaders think and be able to tailor his communication to each leader’s way of reasoning. He should also know what aspects of the business keep the leaders up at night so he can emphasize the features of his team’s solutions which solve them. In meetings with the business, the analytics leader must, at all costs and with all will, avoid the temptation to underscore the technical aspects of his product, and rather elucidate its business features and impacts. Also, as he develops his team, he must be sure to train his members (or the subset that is interested) to be able to effectively communicate with the business.
No Nuance: We Knew This Already
If you talk to most organizations, especially within the manufacturing space, you discover a disappointment of sorts in the prospects of analytics and its ability to drive up the bottom line. Some—a significant portion of—senior executives express their dismay in only meager if any discoveries from the otherwise huge investments in analytics. The only reason these leaders continue to pump money into analytics is because of their unshakable belief in the potency of data analytics, and sometimes, because of its trendiness or peer pressure. However, leaders of analytics cannot count on these executive graces for eternity as they’re bound to dry out, especially during the troughs of the business cycles. Every analytics leader should find it a burden to produce data insights that revitalize the existing operations of the organization and create fresher ones. This is the only way it can survive and thrive within an enterprise charged with maximizing shareholder return. So what does an analytics team have to do to live up to this mantle?
The first thing an organization can do to increase the efficacy of its analytics team is to differentiate Analytics from IT. The duty of producing insights from data (analytics) is starkly different from that of managing the data assets of an organization (IT). While you can structure IT, it’s futile to structure thinking and inquiry (Analytics). This is the predominant reason why most advanced analytics functions placed under IT struggle to produce any defining business or operational revelations.
After you have separated Analytics from IT, the next thing is choosing the right analytics leader. He should have the right combination of aptitudes for business and technical acumen. Interest in business will help him be useful to business leaders, and a technical background can help him better manage and direct his team. The commonest mistake most organizations have made is to think that they can institute a high-performing analytics organization by merely recruiting a Ph. D graduate as the head of the group. Such a choice produces an analytics group that is aloof and irreconcilable with the business needs of the enterprise.
And lastly, the analytics leader has to structure his team so they are working on a variety of projects with diverse complexities. One way to do this is to structure the team by time horizon of projects such as short-term, medium-term and long-term, as has been previously discussed. Another way is to structure them according to the rungs of Davenport’s ladder of analytical maturity: Reporting, Forecasting, Predictive Analytics and Optimization. Such structure forces the analytics team to be able to meet the present and future needs of the organization.
Conclusion:
Politics, HR, Sales, Marketing, Match-making, Mining and all facets of life and enterprise, data is being leveraged to produce magical insights that are enabling exponential levels of clarity, potency, novelty, and above all transformation, call it disruption. Data has essentially become the key yeast without which bottom lines of companies may rise but only minimally, if any. This is why I have called data, big data for that matter, the new big feast of which companies should eat off or suffer economic hunger. The compelling value of data for a 21st century enterprise is now taken for granted by all, even including the incorrigible supporters of guts (They now realize that guts and science are rather complementary). What is not guaranteed is the assumption that the mere institution of an analytics department necessarily produces beautiful results. Thank goodness it’s not that cheap because nothing heavenly is. A lot rests on the shoulders of the enterprise, especially the analytics leader, for the fruits to be reaped. And one of the things he can do to fertilize the grounds for bountiful harvests is to develop a keen emotional intelligence about the business leaders with whom he needs to work closely to demonstrate and realize the value of analytics. Hopefully, the pointers provided in this article help.
