Every company today must digitally transform itself to be ready for Industry 4.0: a business world in which orders come to a company through customers’ smartphones, and products are delivered in digital form to their digital devices. (Think banking, media, software, travel booking, and more.) And even if the products aren’t fully digital, the service that’s delivered is likely to be transacted and consumed with a lot of digital help. (Think Amazon’s cashier-less grocery stores, or the visitors to Walt Disney World in Florida wearing “Magic Band” digital bracelets.)
However, to transform themselves for a digital world and give customers a personalized digital experience, companies must recognize the importance of digital data. Unfortunately, digital data is the overlooked element of digital transformation, akin to electricity in an industrial economy, or oil in the automotive sector.
Without data that can be digitized, processed in volume, dissected to produce new insights about the business, and acted upon quickly, a company cannot digitally transform itself. It needs such data and the ability to use it to constantly monitor its performance for customers, the state of its operations, and its financial condition for the board. Without superior capabilities in managing digital data, that company can’t be responsive enough to customers. It won’t be able to personalize and deliver promotions at the right shopping moment to a shopper’s smartphone. It won’t have an up-to-the-minute picture of the state of its supply chain and customer performance. And it won’t be able to build superb financial systems that can identify revenue leakage, fraud and other problems that can turn into audit (and thus boardroom) and regulatory issues rapidly.
To put it simply, digital data is the DNA that guides digital transformation. It is why such data has become a board-level matter. Companies that can gather, process, analyze and use the digital data that’s coursing through their digital systems are gaining three huge advantages in the marketplace:
• They can generate new revenue faster, recognizing new customer opportunities and evolving new business models with greater agility.
• They are more profitable because they have automated previously manual data processes through Artificial Intelligence (AI) and Machine Learning (ML) (lowering costs and improving revenue recognition while speeding the close).
• They can process more digital data thereby driving more data-based business strategies and enabling them to take calculated growth risks.
However, many companies struggle to maximize the value of the increasingly large volumes of digital data they collect. This is understandable. One reason is that the amount of digital data that companies now collect is huge and growing. An astonishing 90% of the world’s digital data was created in just the last two years—nearly seven decades after the first computers began showing up in offices.1 And the volume of digital data is only going to grow exponentially due to smartphones (born in 2007 and now becoming ubiquitous), the Internet of Things (billions of sensors attached to everything from coffee parts to aircraft engines), and ever more digital devices.
The inability to manage this explosion of digital data is why cloud computing revenue is growing rapidly at such companies as Amazon, Microsoft and Google. In our view, it shows that most large companies understand they can’t afford the enormous amounts of processing power required to process and secure all that data in their own data centers. Those cloud companies were part of a $41 billion public cloud market last year, according to Gartner.2 These essentially are data utilities: huge computing factories that collect, secure, process, and provide tools to analyze and transmit digital data to organizations that need it.
The digital transformation of business— necessary now for competitive advantage and survival—demands exceptional data management capabilities. However, right now, these capabilities are not widespread in the marketplace.
Lagging Digital Data Practices
Most big companies recognize that they’re only fair (or worse) at managing their digital data. In a 2018 Gartner survey of nearly 200 organizations around the world, 60% rated themselves average or below average in using and analyzing their digital data.3 Only 9% would categorize their data maturity at the highest level: (‘transformational’), where data is available to all departments and business units (not sequestered inside them), and analytics practices and tools and insights are shared liberally around a company, enabling the organization to grow while improving internal processes, especially financial ones.
Digital data deficiencies can be seen in four areas:
1. Data collection. Digitally immature organizations lack a coherent data inventory, or ‘data blueprint,’ meaning they aren’t fully cognizant of what data they possess. Although organizations capture huge volumes of digital data in multiple channels about virtually every business process, their products, and customer interactions, many companies have no comprehensive catalog of the data or its state. Every organization has a hierarchy of business processes that rely on data, but many lack an overview of how the datasets they have relate to each of these processes. What’s more, they often don’t know how the data relates to how well the company’s marketing, sales, R&D, distribution and other key business processes are performing. For example, they don’t know what data is most important to identifying a new customer, or convincing that customer to buy. When they look at where they are collecting data, these companies often discover they don’t have nearly the data they need for a superior customer service experience or for demand generation. They typically find they have data siloes (departments that don’t share critical data, especially about customers), missing internal data (information they never thought of collecting before, often because of the cost or impossibility of collecting it (often images and voice), and missing external data—especially from business partners such as suppliers, wholesalers, and retailers.
2. Processing data. When the big data trend erupted, companies sought to take advantage. They collected data from diverse sources internally and externally. It came from partners, public sources, social media, and sensors in connected devices. They availed themselves of falling storage costs, new database technologies and ever-expanding and accessible computing resources and power, including the cloud. But many underestimated how much data they’d have to process. The result is that the computational tasks in numerous large companies require enormous computing power and data center management skills that even the largest IT shops don’t have. That’s why there’s a huge public cloud computing business that is growing rapidly, and some companies are investigating the potential of high-performance and quantum computing.
3. Analyzing data. Even if a company can collect and process all the digital data it must, if it can’t analyze it—ferreting out the trends and other insights that result in more sales, higher profits, more efficient manufacturing and distribution, and so on—the business will suffer. And although many companies have pockets of excellence in analyzing their data, and often have achieved some benefits, those impacts frequently are limited to those functions that invested in analytics skills and technologies. These groups developed ways of structuring and analyzing data that were tailored to their teams’ specific uses, such as marketing, but they have limited applicability to other functions. Extending the benefits of data widely across organizations is critical to improving every business operation and identifying growth opportunities. It’s key to becoming more agile and accelerating decision-making.
4. Making rapid decisions and changes after generating insights on digital data. Recent technology advances, such as AI and cloud computing, present new opportunities to increase ROI, improve business processes, and delight customers. AI enables fast decision-making. Cloud computing provides for the efficient use of computing resources and it facilitates the ability to disseminate data-based insights, whether customer-focused or internal, across an organization. Companies that don’t take advantage of on-demand scalability to manage data within and outside the enterprise and use plug-and-play facilities for intelligent applications, services, solutions, and tools soon will find themselves at a competitive disadvantage, if they are not already.
Let’s now look at how best-practice companies manage their digital data.
Three Steps to Leveraging Data
From our work with dozens of large companies around the world in improving their digital data management capabilities, we have found three steps to becoming proficient at leveraging such data:
Assess the current state by benchmarking the company’s digital intelligence: its capabilities in gathering, processing, analyzing, and acting on data. It should also cover which technologies are currently in use, including whether cloud-based systems are deployed.
People and processes should also be evaluated to determine which skills and positions in the organization are enabling the use of analytics, the processes being used to manage them, and where there may be gaps.
A candid assessment will identify the extent to which leaders embrace the importance of using data to identify strategic opportunities and insights about the business, its competitors and customers. Leaders who aren’t aware of the potential benefits of leveraging digital data can learn. Examples of successful analytics use within the company, information about competitors’ activities, or evidence of what stronger capabilities could mean for the business can help make the case.
Determine where the company should go and map out how to build the capabilities required to get there. The plan should include the technologies that will be used to inform decisions and execute processes. It should factor in the opportunities afforded by cloud computing, including how the company will share data analytics resources internally. Here, the task is to determine a “target state,=.” Key goals for this step include identifying how data will help innovation and ways to introduce automation in the company’s data management and decision-making. Questions to be answered include: Which sources currently are in use? What will be needed in the optimal state? Internally, sources can include customer records from activities in physical and digital channels such as smartphones and websites. Externally, data may be retrieved from partners about their activities with shared customers, and from social media, including consumer sentiment data. New data may come from sources such as Internet of Things (IoT) sensors. Other sources, such as public data from government databases, may also be valuable.
Point the company’s analytics instruments at the strongest opportunities for increasing revenue, improving profitability, and mitigating risk. Establishing consistent models for collecting, formatting, storing, and analyzing data will enable a company to reuse its data in different parts of the organization. Customer data can be useful across marketing, sales, finance, and supply chain management. Using the tools of data analysis is also crucial. A combination of technologies enables companies to process and analyze more data more effectively. This leads to better decisions. Finally, the company must acquire analytics skills, as well as the skills to create a data-driven culture fueled by analytics. That involves creating an immersive analytics environment in which the data trends are brought to life through information visualization, visual analytics, virtual and augmented reality, and intuitive user interfaces.
The key to digital data usage is establishing the means for business experts to collaborate with the data and analytics experts. As with every major change in business culture, the transformation requires leadership from the very top of an organization—i.e., the board of directors.
Transformation requires leadership from the very top of an organization
What Organizations Get When They Master Their Data
The practices we’ve described have top- and bottom-line outcomes. Managing the collection, processing, analysis, and usage of digital data in superior ways leads to superior operational and financial performance of three types: increased revenue, greater profitability, and lower risk. Let’s look at each one.
Superior management of digital data is proving time and again to boost companies’ top line. Examples are numerous.
To increase revenue and improve profitability, Monsanto, the $14 billion agricultural and biotech company, launched a cloud analytics platform it calls “science@scale”. The platform enables the company’s data experts and scientists to collaborate, accelerating the development of analytics-driven decision models, according to Monsanto CIO James Swanson.4 The company estimates it gained $17 million in top-line U.S. revenue and saved $15 million in operating costs because the platform enables scientific work to be completed in hours rather than months.
ABB, a $34 billion industrial firm that services power plants and automates industrial processes, credits its ABB Ability platform of services5 with driving an 11% increase in orders for its software and services in 2017.6 The platform collects real-time data, analyzes, and monitors conditions at factories, oil wells, and power plants to reduce operating costs, and improve safety and maintenance. The company estimates the platform has the potential to generate $20 billion in annual sales.7
Using analytics, AI, and other intelligent technologies, such companies can now gain insights about the health of their business processes and the performance of products in the field. They can use these insights to evaluate and pursue opportunities to monetize data by selling it to organizations that would benefit from it. Above all, the meaning they derive from data can lead to growth, improved profitability, and stronger risk management capabilities.
Insights from digital data can shed light on customer wants and needs permitting a company to offer improved customer experiences and increase sales. These insights lead to the identification of opportunities for growth and more profitable business models, as ABB’s experience illustrates.
“Speed-to-insight,” enabled by cloud-based analytics services, is crucial for organizations to reap the benefits of digital data at volume. Monsanto’s science@scale platform has created savings and generated revenues by accelerating data analysis.
Digital data gives firms new avenues for operational improvements—both back office and customer-facing— and better-informed investments that can improve profitability. For example, a firm can use IoT sensors in its facilities to capture data on its energy usage, then analyze temperatures, occupancy levels, and other conditions to optimize energy consumption and reduce operating costs, growing operating margins.
Siemens, the $88 billion global energy, engineering and manufacturing giant, recently launched a new business using intelligent sensors and software to analyze U.S. railroad data. The insights it derived helped railroad operators anticipate errors and boost performance. (A train that isn’t operating isn’t generating either revenue or profit, but it’s still consuming resources.) The system, an “Internet of Trains,”8 is being deployed by railroads in multiple countries.9
By using data from ecosystems partners, a company can create valuable insights that it can use or sell to others. Monsanto’s Climate Corporation unit has established a hub for agricultural technology firms to sell apps to farmers. They use them to access multiple data streams and track their operations from a variety of viewpoints. With insights from soil moisture sensors, weather data, and satellite imagery, they can improve the way they manage their farms.10 Saving money by reducing wasteful practices is crucial to farmers.
John Deere, the $29 billion maker of farm and construction machinery, partners with Cornell University on analytical tools for farmers. The platform they developed, Ag-Analytics, integrates public data sources with data from John Deere equipment to give farmers insights about field conditions and more, including less expensive conservation practices.11
Digital data, managed well, can also help companies reduce the risks they take every day in their business—in giving credit to poor credit risks, in being in compliance with regulatory authorities, and much more.
Consider HSBC, the $31 billion banking giant. The bank is using AI to automate its compliance processes, including anti money-laundering investigations that traditionally involve thousands of employees. A pilot project working with AI startup Ayasdi saw the number of expensive investigations drop 20%. “We reduce risks. And it costs less money,” HSBC Holdings COO Andy Maguire says.12
Progressive Insurance, the $23 billion automobile insurer, launched a telematics app in 201513 to collect information on customers’ driving. Over the next two years, the company collected 14 billion miles’ worth of driver data, and analyzed it using a machine-learning platform. Progressive was better able to distinguish between safe drivers and riskier ones and reward the safe drivers with an average $130 discount.14
Intelligent systems that enable machines and people to collaborate more effectively lead to more responsive, better decisions. Traditionally, risk managers have based their analyses by examining small data sets to find exceptions to the norm. Such compliance checks have been routine and successfully uncover risks that need mitigation. But risks can sometimes take a long time to surface in small samples. Today’s data storage, management, and analysis technologies make it possible to examine much larger data sets, such as all transactions a bank has processed within a period. Larger samples produce more reliable results, and the more data AI and analytics can work with the more accurate and intelligent they become. This means risks to a company’s reputation, finances, or compliance practices can be detected more quickly and accurately.
The Data Revolution
As these examples show, using digital data effectively can enable a business to make more informed and timely decisions. Automating analytics enables companies to create business processes that require no human intervention, allowing leaders to focus on new opportunities and higher quality decision-making. That is the essence of business agility in the digital age.
Once an organization acquires a comprehensive view of its data landscape and its current capabilities, it can implement programs to achieve the benefits of today’s new and powerful technologies. This journey to more mature data handling and analytics maturity can be broken down into a series of recommended steps.
With a clear roadmap to data and analytics maturity that accounts for advances in data sources, data management, technology, and analytics skills, organizations can prepare to reap the value of their investment and their digital data. But data by itself means little. Companies must make all their data meaningful. That means they must master analytics techniques—approaches to making sense of data—not just data collection and processing alone.
A Way Around Roadblocks
In their efforts to reach data maturity, organizations may encounter many barriers. They may have difficulty realizing consistent benefits and find it hard to align data policies with evolving regulations.
Clarity about data and analytics maturity will emerge from a frank assessment of the organization, as well as what it will take to achieve its target state and realize meaningful benefits from its data. In preparing a roadmap to reach the target state, a company will assess its skills—most importantly the business expertise it requires—and identify the additional skills and talent it will need. It will also identify the digital data streams and formats required for expanding the use of analytics. The technologies (including cloud-based systems) it needs to achieve company goals will become clear.
To ensure a company has strong capabilities in data and analytics across the enterprise, and not simply in isolated pockets, an organization must adopt a vision of analytics for all, combining best practices and frameworks to achieve digital and analytics maturity. It must not limit the use of appropriate tools to select functions or a few experts. The best way to achieve enterprise-wide distribution of analytic insight derived from digital data is to adopt cloud-based systems. These are designed expressly for universal accessibility.
Companies need to pursue opportunities for using their digital data while anticipating regulatory changes. For example, the European Union’s General Data Protection Regulation (GDPR) that went into effect in May 2018 prescribes heavy fines for violations.15 It applies to all organizations doing business and processing personal data in the EU, regardless of where they are headquartered.
To ensure ongoing compliance, companies need to incorporate the conditions of GDPR as they build their analytics capabilities. And they should continue to monitor regulatory environments in all areas of the globe in which the company conducts business.
Joining the Data-First Revolution
In recent years, the siren call of big data launched thousands of analytics efforts across all industries and sectors. Digital-native companies, with data-first mindsets, emerged. They took advantage of the explosion of data and the new technologies available to make it meaningful and actionable.
Now it’s time for established firms to step up. They must assess how good they are at managing their digital data and, with eyes wide open, assess their strengths and identify areas where they need to improve. Companies that are superior at collecting, processing, analyzing, and acting on data are out-marketing, out-selling, out-producing, out-servicing, and outperforming their competitors.
You could say, without exaggeration, that they are outsmarting the competition.
1 IFL Science, July 26, 2017, accessed at http://www.iflscience.com/technology/how-much-data-does-the-world-generate-every-minute/
2 MarketWatch, December 20, 2017, accessed at https://www.marketwatch.com/story/amazon-rules-the-public-cloud-but-google-microsoft-alibaba-are-growing-faster-2017-12-20
3 “Gartner Survey Shows Organizations Are Slow to Advance in Data and Analytics,” press release, February 5, 2018, accessed at: https://www.gartner.com/newsroom/id/3851963.
4 Forbes.com, Monsanto’s CIO Develops Data Science Platform of the Future, October 23, 2017, accessed at https:// www.forbes.com/sites/peterhigh/2017/10/23/monsantos-cio-develops-data-science-platform-of-the-future/
5 ABB Ability, accessed at http://new.abb.com/abb-ability
6 ABB Quarterly Results Press Release, October 26, 2017, accessed at http://new.abb.com/news/detail/2320/q3-2017-results-abb-continuing-growth
7 ABB 2017, Annual Report, Page 24, accessed at http://new.abb.com/docs/default-source/investor-center-docs/annual-report/annual-report-2017/abb-group-annual-report-2017-english.pdf
8 Siemens, Siemens Launches New Business to Digitize the US Rail Industry, Siemens, April 20, 2017, accessed at http://news.usa.siemens.biz/press-release/mobility/siemens-launches-new-business-digitalize-us-rail-industry
9 Siemens website, accessed at: https://www.siemens.com/global/en/home/products/mobility/rail-solutions/services/digital-services.html
10 AG Funder News, What Do Monsanto’s Plans to Open Up Its Digital Platform Mean for the Agricultural Industry?, August 18, 2016, accessed at https://agfundernews.com/what-do-monsanto-plans-to-open-up-its-digital-platform-mean-for-the-agriculture-industry.html
11 Environmental Defense Fund Blog, How John Deere and Cornell Can Ensure Big Data Benefits Farmers and the Environment, October 12, 2017, accessed at http://blogs.edf.org/growingreturns/2017/10/13/john-deere-cornell-data-farmers-environment/
12 Reuters, HSBC Partners with AI Startup to Combat Money Laundering, June 1, 2017, accessed at https://www.reuters.com/article/us-hsbc-ai/hsbc-partners-with-ai-startup-to-combat-money-laundering-idUSKBN18S4M5
13 Progressive, Progressive Insurance Launches Pilot Snapshot Mobile App, September 2, 2015, accessed at https://progressive.mediaroom.com/2015-09-02-Progressive-R-Insurance-Launches-Pilot-Snapshot-Mobile-App
14 Wired, How AI-Driven Insurance Could Reduce Gun Violence, February 27, 2018, accessed at https://www.wired.com/story/how-ai-driven-insurance-could-reduce-gun-violence/
15 EUGDPR.org, “GDPR Key Changes,” accessed at https://www.eugdpr.org/key-changes.html