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Data Analytics for Action & Impact:Transforming Data to Goal-Driven Insight for the Data-Rich yet Information-Poor
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Big data, analytics and the path from insights to value
By Steve LaValle, Eric Lesser, Rebecca Shockley, Michael S. Hopkins and Nina Kruschwitz
In every industry, in every part of the world, senior leaders wonder whether they are getting full value from the massive amounts of information they already have within their organizations. New technologies are collecting more data than ever before, yet many organizations are still looking for better ways to obtain value from their data and compete in the marketplace. Their questions about how best to achieve value persist.
To help organizations understand the opportunity of information and advanced analytics, MIT Sloan Management Review partnered with the IBM Institute for Business Value to conduct a survey of nearly 3,000 executives, managers and analysts working across more than 30 industries and 100 countries.
Among our key findings: Top-performing organizations use analytics five times more than lower performers. Overall, our survey found a widespread belief that analytics offers value. Half of our respondents said that improvement of information and analytics was a top priority in their organizations. And more than one in five said they were under intense or significant pressure to adopt advanced information and analytics approaches.
The source of the pressure is not hard to ascertain. Six out of 10 respondents cited innovating to achieve competitive differentiation as a top business challenge. The same percentage also agreed that their organization has more data than it can use effectively. Organizational leaders want analytics to exploit their growing data and computational power to get smart, and get innovative, in ways they never could before.
Senior executives now want businesses run on data-driven decisions. They want scenarios and simulations that provide immediate guidance on the best actions to take when disruptions occur – disruptions ranging from unexpected competitors or an earthquake in a supply zone to a customer signaling a desire to switch providers. Executives want to understand optimal solutions based on complex business parameters or new information, and they want to take action quickly.
These expectations can be met – but with a caveat. For analytics-driven insights to be consumed – that is, to trigger new actions across the organization – they must be closely linked to business strategy, easy for end-users to understand and embedded into organizational processes so that action can be taken at the right time. That is no small task. It requires painstaking focus on the way insights are infused into everything from manufacturing and new product development to credit approvals and call center interactions.
Top Performers Say Analytics Is a Differentiator
Our study clearly connects performance and the competitive value of analytics. We asked respondents to assess their organization’s competitive position. Those who selected “substantially outperform industry peers” were identified as top performers, while those who selected “somewhat or substantially underperform industry peers” were grouped as lower performers.
We found that organizations that strongly agreed that the use of business information and analytics differentiates them within their industry were twice as likely to be top performers as lower performers.
Top performers approach business operations differently than their peers do. Specifically, they put analytics to use in the widest possible range of decisions, large and small. They were twice as likely to use analytics to guide future strategies, and twice as likely to use insights to guide day-to-day operations. They make decisions based on rigorous analysis at more than double the rate of lower performers. The correlation between performance and analytics-driven management has important implications to organizations, whether they are seeking growth, efficiency or competitive differentiation.
Data Is Not the Biggest Obstacle
Despite popular opinion, getting the data right is not a top challenge that organizations face when adopting analytics. Only about one out of five respondents cited concern with data quality or ineffective data governance as a primary obstacle.
The adoption barriers that organizations face most are managerial and cultural rather than related to data and technology. The leading obstacle to widespread analytics adoption is lack of understanding of how to use analytics to improve the business, according to almost four of 10 respondents. More than one in three cite lack of management bandwidth due to competing priorities.
What Leaders Can Do to Make Analytics Pay Off – a New Methodology
It takes big plans followed by discrete actions to gain the benefits of analytics. But it also takes some very specific management approaches. Based on data from our survey, our engagement experience, case studies and interviews with experts, we have been able to identify a new, five-point methodology for successfully implementing analytics-driven management and for rapidly creating value. The recommendations that follow are designed to help organizations understand this “new path to value” and how to travel it.
1. First, Think Biggest: Focus on the biggest and highest-value opportunities.
Does attacking the biggest challenge carry the biggest risk of failure? Paradoxically, no – because big problems command attention and incite action. And as survey participants told us, management bandwidth is a top challenge. When a project’s stakes are big, top management gets invested and the best talent seeks to get involved.
It’s extraordinarily hard for people to change from making decisions based on personal experience to making them from data – especially when that data counters the prevailing common wisdom. But upsetting the status quo is much easier when everyone can see how it could contribute to a major goal. With a potential big reward in sight, a significant effort is easier to justify, and people across functions and levels are better able to support it.
Conversely, don’t start doing analytics without strategic business direction, as those efforts are likely to stall. Not only does that waste resources, it risks creating widespread skepticism about the real value of analytics.
2. Start in the Middle: Within each opportunity, start with questions, not data.
Organizations traditionally are tempted to start by gathering all available data before beginning their analysis. Too often, this leads to an all-encompassing focus on data management – collecting, cleansing and converting data – that leaves little time, energy or resources to understand its potential uses. Actions taken, if any, might not be the most valuable ones. Instead, organizations should start in what might seem like the middle of the process, implementing analytics by first defining the insights and questions needed to meet the big business objective and then identifying those pieces of data needed for answers.
By defining the desired insights first, organizations can target specific subject areas and use readily available data in the initial analytic models. The insights delivered through these initial models will illuminate gaps in the data infrastructure and business processes. Time that would have been spent cleaning up all data can be redirected toward targeted data needs and specific process improvements that the insights identify, enabling iterations of value.
To keep the three gears moving together – data, insights and timely actions – the overriding business purpose must always be in view. That way, as models, processes and data are tested, priorities for the next investigation become clear. Data and models get accepted, rejected or improved based on business need. New analytic insights – descriptive, predictive and prescriptive – are embedded into increasing numbers of applications and processes, and a virtuous cycle of feedback and improvement takes hold.
3. Make Analytics Come Alive: Embed insights to drive actions and deliver value.
New methods and tools to embed information into business processes – use cases, analytics solutions, optimization, work flows and simulations – are making insights more understandable and actionable. Respondents identified trend analysis, forecasting and standardized reporting as the most important tools they use today. However, they also identified tools that will have greater value in 24 months. The downswings in “as-is” methods accompanied by corresponding upswings in “to-be” methods were dramatic.
Today’s staples are expected to be surpassed in the next 24 months by:
- Data visualization, such as dashboards and scorecards
- Simulations and scenario development
- Analytics applied within business processes
- Advanced statistical techniques, such as regression analysis, discrete choice modeling and mathematical optimization.
4. Add, Don’t Detract: Keep existing capabilities while adding new ones.
When executives first realize their need for analytics, they tend to turn to those closest to them for answers. Over time, these point-of-need resources come together in local line of business units to enable sharing of insights. Ultimately, centralized units emerge to bring a shared enterprise perspective – governance, tools, methods – and specialized expertise. As executives use analytics more frequently to inform day-to-day decisions and actions, this increasing demand for insights keeps resources at each level engaged, expanding analytic capabilities even as activities are shifted for efficiencies.
Sophisticated modeling and visualization tools, as noted, will soon provide greater business value than ever before. But that does not mean that spreadsheets and charts should go away. On the contrary: New tools should supplement earlier ones or continue to be used side by side as needed. That lesson applies to nearly every way that analytics capabilities should be nurtured as an organization becomes more ambitious about becoming data driven: The process needs to be additive. As analytics capabilities are added upstream at increasingly central levels of management, existing capabilities at point of need shouldn’t be subtracted. Nor should they be transplanted to central locations. As new capabilities come on board, existing ones should continue to be supported.
In three distinct areas – application of analytic tools, functional use of analytics and location of skills – we found that adding capabilities without detracting from existing ones offers a fast path to full benefits from analytics-driven management.
5. Build the Parts, Plan the Whole: Use an information agenda to plan for the future.
Big data is getting bigger. Information is coming from instrumented, interconnected supply chains transmitting real-time data about fluctuations in everything from market demand to the weather. Additionally, strategic information has started arriving through unstructured digital channels: social media, smart phone applications and an ever-increasing stream of emerging Internet-based gadgets. It’s no wonder six out of 10 respondents said their organization has more data than it knows how to use effectively.
All this data must be molded into an information foundation that is integrated, consistent and trustworthy, which were the leading data priorities cited by our respondents. Therefore, even though smart organizations will start down the analytics path by selectively attacking the biggest problems (and selectively building out the parts of the data foundation most relevant to gaining insight about them), they’ll need to understand how each piece of this data foundation aligns to an overall information agenda. The information agenda accelerates the organization’s ability to share and deliver trusted information across all applications and processes. It sets up information to serve as a strategic asset for the organization.
The information agenda identifies foundational information practices and tools while aligning IT and business goals through enterprise information plans and financially justified deployment road maps. This agenda helps establish necessary links between those who drive the priorities of the organization by line of business and set the strategy, and those who manage data and information.
Outline for an Information Agenda. The information agenda provides a vision and high-level road map for information that aligns business needs to growth in analytics sophistication, with the underlying technology and processes spanning the following:
- Information governance policies and tool kits: from little oversight to fully implemented policies and practices
- Data architecture: from ad hoc to optimal physical and logical views of structured and unstructured information and databases
- Data currency: from only historical data to a real-time view of all information
- Data management, integration and middleware: from subject-area data and content in silos to enterprise information that is fully embedded into business processes with master content and master data management
- Analytical tool kits based upon user needs: from basic search, query and reporting to advanced analytics and visualization
The information agenda is a key enabler of analytics initiatives by providing the right information and tools at the right times based upon business-driven priorities.
Techniques to Get Started. Hurdles on the path to effective analytics use are highest right at the start of adoption. Here is a way to begin:
- Pick your spots. Search for your organization’s biggest and highest priority challenge, and create a diagram to describe it. Show available data sources, models to be built and processes and applications where analytics will have an impact. Create multiple diagrams if you’re selecting from a strong list of possible initiatives. Keep in mind that your biggest problems, such as customer retention, anti-fraud efforts or advertising mix, are also your biggest opportunities. Change is hard for most, so select an initiative worthy of sustained focus that can make the biggest difference in meeting your most important business goals. Remember that focus is critical during these initial efforts. Do not get distracted once the targeted area is identified.
- Prove the value. Use reason and benchmarks for initial executive sponsorship, but use a proof-of-value pilot to keep sponsors engaged. Estimate how much revenue can be gained, how much money can be saved and how much margins can be improved. Employ techniques to embed analytics to illustrate and prioritize the types of organizational changes that are needed to achieve the value. Pull it all together using an implementation road map with a clear starting point and a range of options for future opportunities.
- Roll it out for the long haul. The challenge should be big, the model insightful and the business vision complete. However, the first implementation steps can be small, as long as they fit your agenda. Reduce your rework by using business analytics and process management tools that you have selected for the long haul – information governance, business analytics and business rules. As you make progress, don’t forget to analyze feedback and business outcomes to determine where your analytics model and business vision can be improved.
To start on the fastest path to value, keep everyone focused on the big business issues and select the challenges that you know analytics can solve today – within an agenda for the future. Build on the capabilities you know you already have. And always keep pressing to embed the insights you’ve gained into business operations.
© Massachusetts Institute of Technology
This is a summary of an article that appeared in MIT Sloan Management Review (Winter 2011). For the complete article see http://sloanreview.mit.edu/the-magazine/articles/2011/winter/52205/big-data-analytics-and-the-path-from-insights-to-value/?type=x&reprint=52205
For the full research report see http://sloanreview.mit.edu/new-intelligent-enterprise/report-analytics-the-new-path-to-value/