How Data Analytics Improves Business Decisions

Any business makes decisions frequently and daily. They decide how to price the product; whom to hire; how to promote; what to stock; what new product to develop; how to focus on projects and investments; and innumerable more. Not always the smartest, most experienced, or even most driven leaders are able to make consistently good decisions. More often than not, it is simply a matter of getting the right information at the right time to the right people.

Data analytics allows you to access the right information in the right time, allowing you to understand what is really happening, why it is happening, what will probably happen next, and what the effect of pre-selected courses of action will be, before you actually make a decision. In a world of complexity and fierce competition that is changing ever more rapidly, data analytics is no longer confined to the technology industry with multi-million dollar investments. It is a requirement for decision makers everywhere who want to beat the competition.

Replacing Intuition with Evidence

For much of history, the business decision-making model has been intuition the judgments of highly experienced people based on pattern recognition, knowledge, and their gut feelings. It has its place. Through true experience, the intuition of a focused, experienced executive contains useful information and it is indispensable in fields where there is too little data to make models work, or where speed is more valuable than accuracy. But it suffers from numerous drawbacks. It is prone to all the well-known cognitive biases those flaws inherent in all human thought, regardless of IQ or experience. It is unable to reason about huge quantities of information at once. It is biased toward recent and exciting experience rather than the limitations of its own memory bank. And it is notoriously incapable of detecting subtle relationships and correlations hidden deep in large data sets.

Data analytics can be used to combat these shortcomings by basing decisions on data. That a retail business might analyze its sales data to learn, which products are selling, which aren’t, and, which customer groups are generating the most profitable revenues, is making decisions based on reality rather than perception. The distinction between reality and perception is often vast, and acting on reality rather than perception in this way is one of the key advantages that businesses with data at their disposal have over those still relying on instinct.

Understanding Customers at a Deeper Level

Perhaps one of the most promising applications of data analytics in business is to gain a richer and more accurate insight into customers their behavioural patterns preferences purchasing trends and motivators. Customer data gathered at every touchpoint web visits, purchase records, process interactions, social media interactions, survey data etc. contains patterns that the eye cannot see but analytical tools can uncover.

Business that make their customer data work for them can differentiate their customers with pinpoint accuracy that which happens to be the most profitable, the most loyal, the most vulnerable to churn and the most ripe for growth. They can send targeted messages and offers to customers rather than spray and pray. They can find the points in the customer journey where friction causes break-off, prevent it and smooth the ride. And they can pre-empt customer needs using predictive analytics to notify that subscription renewal date, suggest products and services or highlight vulnerable accounts.

Optimising Operations and Reducing Cost

Data analytics makes a difference in operational efficiency a business is effective if it’s able to identify its inefficiencies and address them. Manufacturing operations can use sensor and production analytics data to uncover the causes of quality failure, schedule machines offing better maintenance, and plan production for the optimal usage of resources. Logistics and distribution can employ route optimisation tools that add live traffic, weather, and delivery information, to save fuel and reduce delivery times. Retail companies can use inventory analytics to have the right products in the right places to reduce the out-of-stocks (lost sales) and over-stocks (cost-margin).

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The potential financial benefit for operational analytics can be great. Choosing where to make efficiency improvements is much more successful when selecting based on operational data, which shows the greatest opportunities for improvement, rather than on hypotheses about where those opportunities might be. This type of data-focused operational improvement approach, in most companies, reveals gaps and savings that the hypothesis based approach was missing for many years.

Reducing Risk Through Better Forecasting

All business decision entails risk the risk that the market won’t deliver, the that the new product will not gain traction, the risk that the cost assumption is wrong, the risk that the emerging competitor will arrive earlier than expected. Analytics does not remove risk nothing does but it can improve a company’s capability to understand it, manage it, and manage its effects. Forecasting models built upon historical observations, market signals, and economic statistics provide a company with insights about future conditions that are more revealing than intuition about the future can be. Scenario analysis packages allow a CEO to attach financial implications to various assumptions, and stress-test a plan against many realities before going to market.

Credit risk analytics are used in finance to estimate the likelihood of a borrower defaulting. Actuarial data is used in insurance to price risk more accurately. Retailers rely on demand forecasting models to change buying and staffing levels in anticipation of seasonal change. The assumption common to all of these is that the data-informed decision is more likely to be ‘correct’ than the one made without the information.

Making Strategy More Precise and Accountable

At a strategic level, data also delivers a quality of decision-making that was simply not attainable in the pre-data era. Market analytics give insight into competitive position in gscheldyn do we consistently buy more and sell less than our competitors in this segment? Which competitors? Which segments? Pricing analytics help define price points that optimise revenue or margin across defined customer segments in defined competitive environments. Investment analytics provide the evidence on which to allocate catalanoites and e-mails based on seniority of advocacy, not on return. And performance analytics establish the accountability structure in which strategy becomes execution by measuring results against plan, catching deviations early and providing the insights that make course corrections possible before the problems snowball.

The change that data analytics makes possible on the strategic level is the change from art to science in decision-making a process that is repeatable, based on evidence and is reliably more effective, not because the practitioners are more talented but because they work with better information.

Building a Data-Driven Culture

The technical wherewithal to analyze data is an essential but not enough to enable data-driven decision-making. The organisations that will take full advantage of data analytics are those that have developed a culture where data is actually referenced before decision-making and not just referenced post hoc to confirm the conclusion arrived at. This cultural change takes leadership buy-in and demonstration – leadership that leads by example by asking for evidence before forming opinions and taking decisions; that invests in the data structures and analytical skills that enable good analysis, and that fosters an atmosphere where challenging decisions based on data is welcomed.

In addition, data literacy is needed a comprehensive organizational knowledge of the limits and opportunities of data and analytics, including how to decode statistical results without looking for certainty, and how to recognize the potential pitfalls and distortions that can lead even the best analysis astray. Even the most advanced analytics services in the world will probably produce poorer results if executed by teams without the wisdom to use them appropriately than when performed by analytically inclined teams using more modest technology.

The Competitive Advantage That Compounds

The longer-term case for data analysis as a source of superior business decision making is not just that “it makes good decisions.” The case is that it makes many good decisions over time. An enterprise that makes smarter decisions than its rivals about its customers operation price structure, risk profile and strategy will when the magic dust settles produce the right results time and time again, and have more left over to apply every period to gaining even deeper and better analytics skills. The differential between data-for-a-business and gut-for-a-business actually increases over time rather than remaining the same.

In a business environment where decision quality is one of the most crucial factors of competitive success, data analytics is no support function. It is strategic capability one of the most significant ones that an organisation can develop.