You can’t pick up a computer industry publication these days without seeing headlines about how “big data” is changing the corporate climate across a broad array of industries. But is big data and related predictive analytics technology only for large enterprises? Definitely not! The recent explosion of digital data has affected businesses of all sizes and has opened opportunities for companies that adopt machine learning technology (of which predictive analytics is a part) to mine intelligence from data assets.
Predictive analytics has the potential to transform traditional small to medium businesses (SMBs). SMBs have the same desire to take better advantage of their data assets through predictive analytics, but the process with which strategic value can be gleaned is significantly different from the large company. Large public companies and well-funded Silicon Valley start-ups hire staffs of Ph.D. statisticians, computer scientists or even physicists, all highly-paid scientists who find patterns in data and translate them into useful business information. SMBs on the other hand, aren’t able to assemble a data science department. Instead, an outside consultant engages company domain experts and IT personnel to extract and clean data from available silos. Analytics-as-a-service can offer the same actionable results as an in-house team. The end point is the same, whether large company or SMB – previously unknown intelligence from data that translates into business advantage.
Predictive analytics for the SMB can become a vital competitive force by creating new business and drive increased sales. Data-driven companies tend to be better performers with respect to objective measures of operational and financial results. Data-driven decisions tend to be better decisions. Using predictive analytics enables managers to strategize on the basis of evidence rather than pure intuition. Utilizing corporate data assets more strategically leads to better predictions, and better predictions yield better decisions. For example, effective analytics allows the SMB to be quicker and more precise with its promotions like a retailer using a predictive model to optimize returns on advertising spending. The promotions tend to be of higher quality because they’re more granular, and more personalized, and timelier.
Corporate data sets serving as input for machine learning analysis is wide ranging in variety - structured data in transactional databases, as well as unstructured data from social media sources like Facebook and Twitter. Corporate data has a substantial amount of “signal in the noise” simply waiting to be discovered. The ability to effectively utilize these data in a strategic manner is the goal for SMBs wishing to play the same analytics game as bigger members of the corporate ecosystem.
Once the SMB makes take the analytics plunge, it is often the corporate culture rather than reasons of technology that place the effort at risk. Better use of corporate data assets gives the SMB both more panoramic as well as more granular perspectives of their business environment. The ability to see what was previously unseen improves overall strategy, operations, and customer experiences. But mastering that environment means upping your game, finding deliberate and creative ways to identify usable data already in your possession, and exploring surprising sources of information. Managers in the typical SMB may not realize how the information can be used for key decision making. The paradigm change is to view data as a strategic asset. Encompassing huge data sets allows companies to run dozens of statistical tests to identify submerged patterns, but that provides little benefit if managers can’t effectively use the correlations to enhance business performance. Some analytics projects fail because they aren’t in sync with the company’s day-to-day processes and decision making norms. Managers must come to view analytics as central to solving problems and identifying opportunities – to make it part of the fabric of daily operations.
SMBs can eclipse competitors with powerful new business models that derive from an ability to exploit data. As data-driven strategies become rooted in the company, they will become an increasingly important point of competitive differentiation. To fully take advantage of a predictive analytics initiative, the SMB needs to:
- Maintain a clear strategy for how to use data and analytics to compete.
- Be able to identify, combine, and manage disparate data sources.
- Have the capability to build advanced machine learning models for predicting outcomes.
- Enable management influence to transform the organization so that the data and models actually yield better business decisions.
Predictive analytics is likely to become a decisive competitive asset in many SMB industries and a core element in the effort of all SMBs to improve performance. Performance improvements and competitive advantage arise from machine learning models that allow managers to predict and optimize outcomes. The most effective approach to building a model rarely starts with the data; instead it originates with identifying the business opportunity and determining how the model can improve performance. For example, a retail company can generate greater value from customer, product and promotion data collected from stores. The goal is to tailor promotions and other offerings to customers, and to personalize the offers to take advantage of market conditions. The promotions can be higher quality because they’re timelier, more granular, and more personalized.
How does the data science consultant operate within the confines of a SMB? Data scientists are consultants experienced working with large amounts of data using machine learning, statistics and predictive analytics techniques. Data scientists are computer scientists and applied mathematicians who are equally comfortable with equations and speaking the language of business and helping executives reformulate challenges in ways that predictive analytics can tackle. These technologies require a skill set that is new to most IT departments, so hiring a consultant makes the most sense early on to turn the corner toward analytics. Data scientists make discoveries while swimming in data. As these discoveries are made, she communicates what was learned and suggests any implications for new business directions. An important facet of what a data scientist does is to present results in a clear and compelling manner using creative visualization techniques to enhance understanding by executives and managers. A data scientist is an amalgamation of data hacker, business analyst, communicator, and trusted consultant. The most foremost trait of a data scientist is an intense sense of curiosity coupled with a scientific sense for the solution to business problems. This includes a desire to look beneath the surface of a problem, filter out the questions at its heart, and distill them into a straightforward set of hypotheses that can be tested.
The future of big data aligned with the needs of SMBs is very promising. With predictive analytics, managers can measure, and hence identify, radically more about their businesses, and directly translate that knowledge into improved decision making, increased performance, and greater opportunities for competitive advantage. But the power of analytics does not remove the need for vision or human instinct. On the contrary, the typical SMB still needs leaders who can spot a great opportunity, understand how a market is developing, think creatively and propose truly novel offerings, and articulate a compelling vision. The time has come to define a pragmatic approach for SMBs to engage predictive analytics that is tightly focused on how to use data to make better decisions.