The Overlooked Strategic Value of Small Data

' The art of the wise is knowing what not to overlook. '- William Blake

Investors are starting to put a premium on companies that can unlock on the value of data they hold when using to help define their business strategy.

Well before the hyped 'Big Data', such companies have developed competencies to make decisions based on the information they already have – from managing to analyzing data in ways that enhance their understanding of their business, to converting insights from statistical models into real day- to-day operational changes to support the overall implementation of their strategy.

In fast-moving environment, being able to prioritize strategic opportunities is primordial and plain evidence-based decision-making is the tool. Seven-Eleven has been the most profitable retailer in Japan for more than 30 years because it provides a lot of little data to its part-time sales employees to help them make better operating decisions on a daily basis, extremely translating into better strategy decisions.

When creating data-driven strategies to innovate, compete and capture value businesses often aspire to the world of 'Big Data', forgetting that similar rewards can be achieved through managing their existing 'Small Data' in a more strategic manner.

1. Improve operations

At the heart of DP world's growth (now the world's 4th largest port operator with 65 terminals in 31 countries) is the ability to control, measure and improve the movement and placement of every box, crane and truck. Sensors monitor the operations through a staggering total of 246,000 steps, letting operators manage and optimize traffic at every point.

Focus on efficiency improvement helped DP save more than US $ 50m on activities never previously identified as a waste. Rather than rely on best practice, DP leverages its data to calculate the most efficient way to complete a job or identify heavily used equipment with a low productivity yield.

2. Transform product development decisions from a subjective to an objective exercise

Using controlled experiments, companies can test hypotheses and analyze results to guide strategy decisions and operational changes. Leading on-line companies, such as Google or Amazon, are continuous testers. They allocate a set proportion of their web page views to conduct experiments that reveal the factors that drive higher user engagement or sales gains.

Some of McDonald's stores track operational data such as customer interactions, traffic in stores, and ordering patterns. Researchers can model the impact of variations in menus, restaurant designs, and training, among other things, to improve productivity and sales.

3. Expand customer relationships, including better tailor products or services

Across all its brands, P & G is using computer modeling and simulation to analyze multiple data sources such as consumer sales data, shipping data and information from the company's digitized processes to help drive business decisions, such as product provisioning on a daily basis.

Netflix, the video-streaming website, has used its vast database of user researches, views, pauses, and reviews to design the made-for-the-internet series 'House of Cards'. The series combined a popular director (David Fincher), actor (Kevin Spacey) and plotlines borrowed from a popular British show with the same title – all of which ranked highly on Netflix popularity metrics.

4. Enhance strategic decision-making and minimize risks

For many years, ExxonMobil has collected and processed 3-D images of geologic formations beneath the earth's surface and, today, its engineers use 4-D analysis (which shows changes in a field over time) to further reduce the costs and risks of exploration . In the oil fields, instruments regularly read data on wellhead conditions, pipelines, and mechanical systems to allow real-time adjustments to oil flows, optimizing production and minimizing downtimes.

5. Identify and create new business opportunities

A manufacturing company learned so much from analyzing its own data as part of a manufacturing turnaround that it decided to create a business to do similar work for other firms. Now the company aggregates shop floor and supply-chain data for a number of manufacturing customers, and sells software tools to improve their performance.

Montesanto has acquitted the Climate Corporation with more than 30 years of weather data, 60 years of crop-yield data, and multiple terabytes of information on soil types. With that reservoir of historical information and sophisticated algorithms, the company now offers fee-based advice to farmers through an intuitive online portal.

The failure of many companies' investment in the eponymous' Big Data 'is often driven by their failure to do a good job with the information they already have. Capturing the potential of data analytics requires a clear plan that establishes priorities and well-defined pathways to business results, much as the familiar strategic-planning process does.

Often the value of data is unknown until you ask a question of it, test that question and then deduce an even better, more relevant question, also bearing in mind that the same pool of data can yield a variety of insights. The Holy Grail is not just an answer to specific questions, but the process itself – a cycle of experimentation that constantly reexamines data in search of new ways to extract its intrinsic value. And until a company learns how to use data and analysis to support its operating decisions, it will not be in a position to benefit from 'Big Data'.

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