RSM Global

Big data and the middle market

Opportunities for middle market businesses through the emergence of Big Data.

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Rapid technological shifts have spurred the emergence of Big Data as a powerful business tool for applications across a broad range of industries. The global expansion of smart mobile phones (1.5 billion sold in 2015 alone) has dramatically heightened Internet traffic and social media exchanges (31 million Facebook messages per minute). Parallel advances in the Internet of Things (IoT) have enabled the linkage of physical objects to digital networks, boosting data flows from home appliances, motor vehicles, and industrial machinery. Meanwhile, gains in wireless broadband and cloud computing have expanded global capacity to manage the deluge of digital data streaming from individuals, households, and organisations (RSM Global Economic Insights, “The Internet of Things”, August 2016).

This article explores the use of Big Data by middle market businesses, whose adoption of these new technologies lags behind that of other sectors of the international business community. 

Big data technologies

The emerging field of “Big Data” subsumes a group of related technologies designed to collect, process, and analyse the large and growing volumes of digital data in the global economy. These technologies include the following:

Data warehouses: Centralised, specialised data repositories to store large amounts of structured data

Distributed systems: Decentralised data processing platforms such as Apache Hadoop and open source software 

Stream processing: Technologies to process real-time streams of event data such as financial trading, fraud detection, and process monitoring

Structured query language: SQL software to manage information in relational databases

Visualisation: Technologies that translate data analyses into animations, images, and diagrams 

Commercial potential of big data

These Big Data technologies offer significant promise for business enterprises across the global value chain. Manufacturing companies can use data analytics to generate operational efficiencies, improve supply chain management, and accelerate new product development. Oil and gas companies can employ remote monitoring systems to manage rigs and pipelines equipped with data-emitting sensors. Utility companies can extract data on household electricity consumption to align supply and demand of distributed power. Healthcare providers can tap large data bases to assess the efficacy of medical treatments and improve patient outcomes. Retail companies can leverage Big Data platforms to track in-store consumer behaviour, optimise product assortment, and rationalise pricing. 

A recent study by McKinsey & Company (McKinsey Quarterly, “Digital in Industry: From Buzzwords to Value Creation”, August 2016) estimates that companies adopting Big Data technologies stand to reap the following gains:

  • 10-40 per cent reduction of equipment maintenance costs
  • 20-50 reduction in time to market
  • 30-50 per cent decrease in machine downtime
  • 45-55 per cent increased productivity in technical professions
  • 85 per cent increased market forecasting accuracy

However, the commercial potential of Big Data is very far from realisation. As a consequence of advances in digital technology, more data has been generated during the past two years than in all of prior human history. The International Data Corporation estimates the current volume of global data at 2.8 zettabytes. 

However, just 0.5 per cent of this massive body of data has actually been analysed–demonstrating the technical, organisational, and human constraints on the ability of companies to utilise the vast body of digital data now available to them.

Furthermore, most of the commercial value of Big Data has been captured by a relative handful of large companies. Foremost among these Big Data beneficiaries are “born through analytics” IT and e-commerce companies such as Amazon, eBay, Facebook, and Google. Other earlier adopters of Big Data technologies include global technology companies (Dell, IBM), large industrial corporations (General Electric), global retail companies (Walmart, Target, Tesco), international hospitality chains (Harrah’s, Marriott), branded consumer companies (Zara), and leading financial service companies (Capital One, Progressive Insurance).

Fulfillment of the global promise of Big Data thus depends on the dispersion of the above-noted technologies beyond this elite circle of early adopters.

Big data adoption by middle market businesses

Middle market businesses have been slow to embrace Big Data. A recent survey by Gartner Inc. indicates that mid-sized companies (defined as business organisations between 1,000–9,999 employees) lag behind both large corporations and small enterprises in adoption of Big Data technologies. 

Just 5 per cent of mid-sized companies in the Gartner survey are at deployment stage of Big Data, compared to 21 per centof large companies and 23 per cent of small enterprises. The largest share (35 per cent) of middle enterprise are at the initial knowledge gathering and strategy development phases of their Big Data programmes  (Gartner, “Survey Analysis: Big Data Investments Begin Tapering in 2016”, September 19, 2016).

The following factors impede the adoption of Big Data technologies by middle market businesses:

Scale
Many middle market businesses lack the scale to make effective use of digital data. For instance, many mid-sized manufacturers do not possess a critical mass of sensor-equipped machinery to generate a requisite volume of information to apply data analytics for operational performance improvement. Similarly, mid-sized consumer product companies often lack the internal capacity to collect and process data on market trends and buyer preferences. 

Complexity
The high levels of complexity of Big Data technologies–entailing the application of sophisticated software programmes  and advanced data analytics to make discriminating use of data streams across multiple channels–deter middle market businesses anchored to legacy IT systems.

Human capital
Adoption of cutting edge Big Data technologies demands employees with highly specialised education and training. McKinsey estimates that the United States will face a shortage of 140,000–190,000 Big Data experts by 2018. Middle market businesses competing with large companies like GE and IBM may be ill positioned to attract this scarce talent. 

Risk aversion
The surprising result of the Gartner survey–that small enterprises (fewer than 1,000 employees) are outpacing mid-sized companies in Big Data implementation–suggests a measure of risk aversion in the middle enterprise sector. Lacking either the scale of large corporations for major IT deployments or the appetite of small enterprises for innovative technologies, middle market businesses hedge on Big Data investments. 

Applications of big data technologies

To surmount these barriers to Big Data adoption, middle market businesses should examine the experiences of early adopters in key global industries:

Retail
As the world’s largest retail company with a huge installed base of customers, stores, and suppliers, Walmart is driving the application of Big Data technologies in the retailing sector. Walmart deploys its Retail Link system to manage product inventory and stocking across the company’s global supplier network (17,400 vendors in 80 countries). Walmart’s Data Café platform tracks point of sale data (1 million transactions per hour) to optimise product assortment and delivery across retail stores worldwide. These Big Data technologies enable Walmart to formulate dynamic algorithms to assess shifting consumer preferences and customise outlet layouts based on store-specific sales data and buyer behaviour.

Industrial internet
Leveraging its standing as a leading global supplier of industrial products, General Electric has launched a software platform (Predix) that permits the analysis of data streams from Internet-connected sensors in equipment and machinery. GE’s initiatives in the “Industrial Internet” target fixed asset-intensive industries such as aerospace (predictive maintenance of jet engines), energy (monitoring of power stations), and mining/extraction (surveillance of downhole drilling and offshore operations).

Life sciences 
Cleveland Clinic, Kaiser Permanente and other healthcare providers are deploying Big Data systems to improve delivery of services to patients. Applications in the healthcare industry include (1) remote monitoring and treatment of patients with chronic diseases, (2) prescriptive analytics to match individual patient needs with customised interventions, and (3) correlation of data from multiple patient pools to gauge the efficacy of cancer drugs and other therapies.

Meanwhile, Merck and other biopharmaceutical companies are applying Big Data technologies to compress the long and costly process of developing, testing, and approving new drugs. These technologies facilitate the selection, stratification, and monitoring of participants in clinical trials and hasten the analysis of clinical data for regulatory approval. 

Energy and utilities
Big Data technologies enable the capture of real-time information on energy consumption by individual households. This data permits the adjustment of electricity supplies and prices to meet variations in power demand and calibrate utility bills with the usage profiles of customers. 

A pioneer in the application of Big Data in energy and utilities is OPower, the Virginia-based technology company that was acquired by the California software giant Oracle in May 2016. OPower’s digital platform allows power companies to process large volumes of residential energy data (collected from millions of smart meters worldwide) and analyse household energy consumption patterns (based on load levels, time, geography, and weather). 

Transportation and logistics
Big Data presents manifest possibilities in transportation and logistics: visualising delivery routes, rationalising distribution networks, maintenance of transportation vehicles. For example, United Parcel Service has launched ORION (On-Road Integrated Optimisation and Navigation) that employs proprietary software, advanced package labelling, and GPS-enabled fleet telematics to optimise distribution routes.

Big data opportunities for middle market businesses

The experiences of large companies described above provide valuable guidance for middle market businesses considering investments in Big Data technologies:

  1. As late movers in the Big Data arena, middle market businesses are well positioned to learn from the experiences of large early adopters. For instance, mid-sized manufacturing companies can survey the results of Big Data deployments by large manufacturers to identify specific applications that failed and/or succeeded to achieve satisfactory returns on investment. Mid-sized biotechnology firms can evaluate the experiences of large pharmaceutical companies for indicators of how best to leverage Big Data technologies for accelerated drug development.
  2. Consistent with long-term trends in the ICT industry, Big Data technologies are simultaneously rising in quality and declining in cost, for example, ultra-miniature, high-powered, Internet-enabled sensors that were once prohibitively expensive are now within reach of budget-constrained middle market businesses. The expansion of cloud computing and forthcoming deployment of 5G broadband will expand opportunities for middle market businesses with limited internal capacity to externalise Big Data applications. 
  3. Middle market businesses are not only potential users of Big Data technologies, drawing on the prior experiences of large early adopters. They are also prospective providers of Big Data technologies and services to those very same large companies. For instance, retail companies and consumer goods manufacturers exhibit a growing demand for specialised software products capable of discriminating amid massive of data sets, creating a market opportunity for mid-sized software companies. 
     

This article was written by David Bartlett
Executive in Residence
Director of Global and Strategic Projects
Kogod School of Business
American University
Washington, D.C.

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