Big data is big business. A study by Hexa Reports concludes, “In 2017, ‘Big Data’ vendors will pocket over $57 Billion from hardware, software and professional services revenues. These investments are further expected to grow at a CAGR of approximately 10% over the next three years, eventually accounting for over $76 Billion by the end of 2020.”[1] It should come as no surprise that big data is big business. In the digital age, data is business. Leandro DalleMule, Chief Data Officer at AIG, and Thomas H. Davenport (@tdav), a Professor in Management and Information Technology at Babson College, assert, “More than ever, the ability to manage torrents of data is critical to a company’s success.”[2] In spite of the necessity for companies to leverage data wisely, DalleMule and Davenport report, “Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions — and less than 1% of its unstructured data is analyzed or used at all. More than 70% of employees have access to data they should not, and 80% of analysts’ time is spent simply discovering and preparing data. Data breaches are common, rogue data sets propagate in silos, and companies’ data technology often isn’t up to the demands put on it.” Their bottom line is this: “[Companies will never achieve their full potential] in the absence of a coherent strategy for organizing, governing, analyzing, and deploying an organization’s information assets.”
The Need for a Good Big Data Strategy
DalleMule and Davenport assert a good big data strategy must consider both defensive and offensive use of data. They explain what those sub-strategies entail:
“Data defense and offense are differentiated by distinct business objectives and the activities designed to address them. Data defense is about minimizing downside risk. Activities include ensuring compliance with regulations (such as rules governing data privacy and the integrity of financial reports), using analytics to detect and limit fraud, and building systems to prevent theft. Defensive efforts also ensure the integrity of data flowing through a company’s internal systems by identifying, standardizing, and governing authoritative data sources, such as fundamental customer and supplier information or sales data, in a ‘single source of truth.’ Data offense focuses on supporting business objectives such as increasing revenue, profitability, and customer satisfaction. It typically includes activities that generate customer insights (data analysis and modeling, for example) or integrate disparate customer and market data to support managerial decision making through, for instance, interactive dashboards.”
They go on to note that it’s important to strike the right “balance between defense and offense and between control and flexibility.” Achieving that balance is not as easy as one might think. “It’s rare,” they write, “to find an organization — especially a large, complex one — in which data is both tightly controlled and flexibly used.” To help companies to try and find that balance, they have embedded in their article a tool called “Assess Your Strategy Position” that offers “diagnostic questions to help business executives place their companies on the offense-defense spectrum and gauge whether their data strategy aligns with their corporate strategy.” Randy Bean (@RandyBeanNVP), founder and CEO of NewVantage Partners, reports companies that have adopted a good data strategy are starting to see positive results from their efforts.[3] He goes on to list ways those organizations are leveraging big data. They include:
- Decreasing expenses
- Finding new innovation avenues
- Launching new products and/or services
- Adding revenue
- Increasing the speed of current efforts
- Transforming the business for the future (i.e., digital transformation)
- Establishing a data-driven corporate culture
Bean agrees with DalleMule and Davenport that leveraging both defensive and offensive activities is necessary. “Big data isn’t just being used for cost-cutting.” he writes. “[A survey of executives of Fortune 1000 companies] strongly indicates that firms are also undertaking ‘offensive’ efforts that are explicitly intended to change how they do business.” The offensive use of data draws the most attention because it has the potential to disrupt. Aashish Kalra (@aashishkalra), Chairman of Cambridge Technology, explains, “Big Data Intelligence is transforming the ways businesses function — from transactional to relationship basis. The adoption of Big Data in understanding consumer behavior helps any enterprise understand its most important customers and competition. Big Data can be effectively used to show the right products for the right consumers at the right time, and to identify any irregularities in the sales patterns. Big Data can bring about a radical growth for any enterprise by predicting the current trends accurately. For sellers and producers too, this technology fuels analytics which can help in predicting demand or shift in demand.”[4]
Developing a Big Data Culture
Bean asserts, “At this point in the evolution of big data, the challenges for most companies are not related to technology. The biggest impediments to adoption relate to cultural challenges: organizational alignment, resistance or lack of understanding, and change management.” Adrian Bridgwater (@ABridgwater), reports that Teradata has identified “seven key ‘cultural elements’ of big data that express how firms should culturally curate and embrace the potential business advantages that are on offer.”[5] They are:
1. Encourage an environment that fosters business outcomes and experimentation. “Teradata president and CEO Victor Lund argues that firms who want to bring a big data analytics culture into the workplace need to think about it as a process of business experimentation, but one that has a notion of what might be possible on the road ahead. ‘Firms need to have some notion of what business outcomes they want to get to in the first instance before they start to bring data analytics culture online, said Lund. Even if those business goals change over time (as they inevitably will), firms need to have an idea what new markets, new working methods and new efficiencies they think they want to achieve.”
2. Look for repeatable solutions. “A lot of big data analytics processes have been applied by other businesses in similar use cases. … We can automate what happens to certain types of data for certain data workloads based upon predefined models that have been in use elsewhere.”
3. Build applications around analytics. “Firms need to build their software applications around a central connection to data analytics itself. If this happens (and yes, it is a big if) then there is (arguably) a higher probability that the business function in any given organization will start to accept the importance of data analytics in terms of a) feeding data into it and b) using the insight results it is supposed to offer.”
4. Optimize big data analytics to work in multiple operating environments. “To make big data analytics work effectively, analytics engines need to be able to span multiple data formats, work with multiple types of data storage (on-premises cloud, public cloud, hybrid mixtures — and different data disk types too) and multiple data types (structured data, semi-structured data and messy hard to classify or digitally quantify unstructured data). … It’s all about being able to work in multiple data operating environments.”
5. Ensure big data applications are cloud compatible. “For big data analytics to work, culturally, it has to be able to work anywhere. What this means in technical terms is that it has to work with the way firms are using cloud computing today.”
6. Embrace thick data. “[Thick data] is defined as qualitative information that provides insights into the everyday emotional lives of consumers. It seeks to go some considerable way beyond big data as it attempts to explain why consumers have certain preferences, the reasons they behave the way they do, why certain trends stick and so on.”
7. Ensure big data projects are scalable and can be orchestrated. “Big data analytics clearly has be able to scale upwards when it is shown to be working well. A degree of orchestration will also be needed to bring together big data analytics workloads that happen in different locations.”
A good cognitive computing platform can help companies pursue a digital culture because it can gather, integrate, and analyze both structured and unstructured data. It can help provide the orchestration to which Bridgwater refers and provide actionable insights in plain language to decision makers. Bridgwater concludes, “If we can embrace the shape of these seven culturally-charged elements of big data analytics, then firms can start to use analytics to work smarter and improve their bottom line.”
Summary
Bean notes, “Big data is already being used to improve operational efficiency, and the ability to make informed decisions based on the very latest up-to-the-moment information is rapidly becoming the mainstream norm. The next phase will be to use data for new products and other innovations.” If cultural gaps can be closed, the future of digital enterprises looks bright. Kalra explains, “The impact of Big Data is visibly impacting customer relationships, product development, geographic expansion, product expansion and brand expansion, organizing business operations, optimizing the supply chain and fundamentally changing the way growth engines are modeled. With Big Data’s applicability to unstructured data there have been considerable improvements in customer experience, reduced response time, time to resolution, effective management of stakeholders including employees, developing a data focused business model, and decision making. In the years to come, Big Data will reinvent, and eliminate cumbersome business processes that lead to time lag. Enterprises will scale new heights by exploring this untapped data and extract incremental value. Big Data will affect almost all human activities ultimately influencing the way businesses chase growth, to positive effect.”
Footnotes
[1] Hexa Reports, “Big Data Market Is Forecasted To Grow Over $76 Billion By 2020: Hexa Reports,” Press Release Rocket, 12 May 2017.
[2] Leandro DalleMule and Thomas H. Davenport, “What’s Your Data Strategy?” Harvard Business Review, May-June 2017.
[3] Randy Bean, “How Companies Say They’re Using Big Data,” Harvard Business Review, 28 April 2017.
[4] Aashish Kalra, “Need for Big Data Intelligence in an Enterprise World,” BusinessWorld, 1 May 2017.
[5] Adrian Bridgwater, “Teradata: The 7 Pillars Of Big Data Culture,” Forbes, 4 April 2017.
