BY BRUCE QUADE, CHIEF EXECUTIVE OFFICER
ABOUT BIG DATA:
CONVERTING DATA INTO YOUR
MOST VALUABLE ASSET
INTRODUCTION
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2
DATA AS AN ASSET
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3
MAXIMIZING DATA’S VALUE
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4
THE C-SUITE’S ROLE
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5
CHALLENGES ARE EXPECTED AND MANAGEABLE
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5
CONCLUSION
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7
ABOUT SAGENCE
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8
INTRODUCTION
Imagine if you had an asset – for example, intellectual property – that was abundant, growing exponentially, proprietary, and could quickly be used to increase revenue and improve profitability. What would you do to exploit it? How would you organize to ensure it produced maximum value? What investments would you make to systematically acquire, develop, manage, renew, and protect it? These are questions most business leaders should be asking themselves because, in fact, they already have this valuable asset. What is it? Their organization’s data. And while Big Data has organizations thinking, most still are falling short in a strategic and systematic approach to maximize the value of their data.
Companies that inject big data and analytics into their
operations show productivity rates and profitability
that are 5% to 6% higher than those of their peers.
“Making Advanced Analytics Work for You” by Dominic Barton and David Court, Harvard Business Review Oct 2012
DATA AS AN ASSET
Business managers are responsible for converting assets into revenue and profits. Whether the assets are current or non-current (accounts receivable, inventory, plant and equipment, people, patents), successful businesses have strategies and management systems to generate revenue and profits through effective use of these assets. Consider some of the more traditional forms of asset management that have a systematic means of acquiring, evaluating, deploying, developing, and maintaining assets – for example, plant and equipment and people and intellectual property. Over time, entire departments have grown to ensure the productivity of these critical assets.
Now consider another asset that has tremendous potential to be systematically used to create revenue and profitability for an organization: its data. This asset has unique characteristics setting it apart from all other organizational assets. For example: ABUNDANT – Organizations today have spent decades automating the majority of their functions and processes resulting in large investments in information technology and growing IT organizations. One result of that investment is growing electronic repositories of data across the organization including financial, product, order, customer, logistics, and vendor data. Add to that the advent of social media, and there are many places to look for value in your data.
DATA AS AN ASSET
(CONT.)GROWING DAILY AND EXPONENTIALLY – The underlying automation or transaction systems are operational and, therefore, throw off new data every day. Just as there is a compounded annual growth rate (CAGR) for business activities, there is a similar CAGR for data. And the sources of data are also growing through, for example, mobile applications, social media, and sensors. PROPRIETARY – This characteristic is perhaps the most important. Because data assets are a byproduct of your systems and daily transactions and because they are only captured and stored for your use, like a patent or other intellectual property, they are proprietary. No one can know what you know about your customers, your products and services, and your operations. The implication is that whatever insights you can gain from your data that you can convert into actions affecting revenue and profitability, become much more difficult to be competed away.
QUICKLY CONVERTED – Data traditionally has been viewed as a by-product of information technology. It was the exhaust of transaction systems and was not viewed strategically. A strategic view would see data as fuel. Unlike some assets, such as a physical plant or intellectual property, the ability to convert this asset into revenue and profits through new insights and actions, does not require large investments in capital and time. Those investments have usually been made in the application systems, data warehouses, and analytical tools of the organization.
Of course, most companies today are making some use of their data assets. Data is used in activities to measure the health of a business – for example, operational reporting and key performance indicators. And companies are increasingly performing more sophisticated levels of analysis, with Big Data often leading the charge. But given the unique characteristics of this asset, are organizations maximizing its value? Data must be used to create new insights that will result in new decisions and new actions. A data warehouse alone will not accomplish this, nor will sophisticated analysis that does not lead to new actions. Companies need their own strategies and management approaches to systematically maximize the value of this asset.
Harnessing the power of your data creates game-changing competitive strategies. Harrah’s Casino made a decision that it would com-pete based on a deep knowledge of its customer, rather than through large capital outlays for bricks and mortar, to build new proper-ties. Through the use of its customer loyalty program, Harrah’s became highly skilled at acquiring data about its customers and analyzing it to create new competitive advantages. In effect, the company created a new class of asset to compete with a more traditional class.
EXPECT
ATIO
NS
Plateau will be reached in: Less than 2 years 2 to 5 years 5 to 10 years
*AS OF JULY 2014 More than 10 years
Speech Recognition Consumer Telematics 3D Scanners Enterprise 3D Printing Activity Streams In-Memory Analytics Gesture Control Virtual Reality NFC Cloud Computing Mobile Health Monitoring
Machine-to-Machine Communication Services Augmented Reality 3D Bioprinting Systems Gamification Smart Robots
Hybrid Cloud Computing Affective Computing
Content Analytics
In-Memory Database Management Systems Big Data
Data Science
Internet of Things Natural-Language Question Answering
Smart Workspace Virtual Personal Assistants Digital Security Bioacoustic Sensing Volumetric and Holographic Displays
Software-Defined Anything Quantum Computing Human Augmentation Brain-Computer Interface
Wearable User Interfaces Consumer 3D Printing Cryptocurrencies Complex-Event Processing Speech-to-Speech Translation Autonomous Vehicles Smart Advisors Prescriptive Analytics Neurobusiness Biochips Quantified Self Connected Home
THE RULE AND ITS
CHALLENGES
CONTINUEDInvestment banks and FCMs (Futures Commission Merchants) are only beginning to change business practices due to counterparty exposure the FCM has to other firms when their controls fail – e.g., MF Global. In the past, FCMs focused on top-line growth, often ignoring data integrity that would have otherwise enabled stronger monitoring of counterparty exposure and customer risk monitoring. Lax controls meant accepting trades that were given regardless of whether they had the proper information that was required for a fully established client. The FCM would accept the give-in trades and worry about the “details” later.
This is no longer an option with Rule 1.73, which requires FCMs to establish risk-based limits with all proprietary and customer accounts. In addition, FCMs are held responsible if controls and/or processes fail. As a result, the FCM can no longer accept a trade unless it can properly identify customer and counterparty legal entity identifications (LEIs) and can confirm that limits have been communicated.
Communicating limits to existing customers/counterparties is straightforward, but checking to see if limits have been established on incoming trades prior to clearing creates a different set of challenges. This requires pre-clearing trade systems (order management systems), if available, to identify a customer and counterparty (and their assigned limits) to confirm that the trade does not breach limits. Prior to this rule, many FCMs focused on post-trade monitoring for controls but have since shifted to pre-cleared. Vendors have begun to offer solutions for advanced pre-cleared trade monitoring, SunGard being one of the first to adapt to 1.73, but no vendor solution will prove beneficial unless the FCM can secure the accurate data that is needed to appropriately identify a particular customer/counterparty in all data stores.
SOLUTIONS FOR FCM
sData integrity is critical to address and maintain adherence to regulatory requirements. After years of consolidation in the industry, FCMs need to focus on the integration of multiple internal data warehouses and platforms in addition to data from exchanges with international reach. (Each exchange often has its own data symbology and structural issues.)
Reference data vital for reporting and monitoring is often scattered and unstructured throughout a firm with minimal intervention and maintenance, which often creates confusion between the different internal business lines. There is minimal standardization between the internal data sets, which leads to a significant lack of integrity. Often, different business lines within the same firm may store the same or similar data in different systems with dissimilar hierarchies and structures. The absence of data integrity may lead to false reporting, which can hinder projects, damage relationships with clients, and raise red flags with regulatory bodies (causing the failure of internal and external audits).
Financial firms are preparing for the difficult questions asked
by regulatory bodies and customers:
• What methods/variables are used for reporting?
• How do firms evaluate the quality and completeness of their reporting results?
• How do firms come to understand the valuation methods and assumptions around vended data?
• How are the internal processes for reporting documented and supported?
• What is the frequency with which firms review internal and vended data?
• What transparency tools do firms have to understand the data that they are consuming and reporting on?
MAXIMIZING DATA’S VALUE
The good news is that companies do not have to build large organizations, complex strategies, or complex systems to get started. Parallel paths can be taken to develop systematic approaches to maximize the value of their data. Companies can begin with a tactical approach by isolating an area that presents a particular challenge or opportunity to the business. Start now by asking some specific questions about your business. For example, why are we losing revenue in this product line, and can we predict it? Are there pricing anomalies that make some products less price-sensitive? How can we optimize the return in our marketing spend against the customer life cycle? While companies often have hypotheses that attempt to answer these questions, they must be developed in a way that can be verified or refuted with rigorous, fact-based analysis. Create models to test the ideas and isolate the data that will be needed in those models. If done well, the process can be repeated to continually test and refine the organization’s hypotheses. It also can be used to test and verify new actions a company has taken based on the findings from the analysis. Finally, just the process of this tactical approach will result in learning more about your data, analytic capability, and creativity in thinking about the business. This learning will serve as great input to the other parallel path companies can follow – the strategic approach.
The strategic approach assumes a top-down organizational commitment to developing the strategies, management practices, and processes needed to use analytics and the firm’s data assets as a way to strengthen its competitive position. Remember, one of the unique attributes of these assets is their proprietary nature. No competitor can know what you know about your business and your customers.
THE C-SUITE’S ROLE
From the point of view of the CEO and his or her management team, it may start with a review of how an organization manages its performance and uses data when developing strategy. What metrics are used to do this? How well can a company decompose those measurements for greater insights? How well do those measurements reflect a shared performance approach rather than a measurement of individual functions in a business? When faced with new facts that are counterintuitive to the many years of experience of a senior team, does it result in decisively new action? So, for the executive team, it is an assessment of not only how your data is used to run the day-to-day operations of the organization, but how it is used to steer the organization, refine its strategies, and develop new objectives for the business.
The organization management teams committed to this path also will take stock of their capabilities along a number of dimensions including technology investments; data quality, access, and availability; analytic skills and tools; and decision-making processes including the culture and incentive structures of the organization. Knowing strengths and weaknesses along these dimensions is the first step in creating a strategy and plan to move the organization to increasing levels of sophistication.
The strategic approach assumes a top-down organizational
commitment to developing the strategies, management
practices, and processes needed to use analytics and the firm’s
CHALLENGES ARE EXPECTED
AND MANAGEABLE
Creating the strategies and management systems to ensure your data is one of your most productive assets will not be without its challenges.
To begin with, the sheer volume of data an organization has accumulated can be overwhelming. The quality of that data should be assessed, and the organization will need a way of governing its acquisition and usage. In addition, ongoing management and maintenance of the asset is needed to ensure its integrity and usability.
Another critical area is data analysis – for example, understanding data and its relationships, identifying new data elements that can be derived from existing data, or identifying external sources of data that can augment your existing data. What can make this challenging for organizations is the lack of experience in thinking about data in this way.
Finally, there will be organizational and behavioral challenges along the way. Companies often need to think “outside the silos” when deriving new insights about the business, because between the silos is often where those new insights are found. And if an organization wishes to affect new actions from these insights, behavioral alignment and incentive structures may also require change. More and more companies are facing these challenges head on. And the emergence of the Chief Data Officer is a leading indicator that companies are getting on board.
CONCLUSION
Becoming highly skilled at converting your data into your most valuable asset and, in turn, creating a strong competitive advantage will have its challenges. The good news is that your organization is not alone in this undertaking. Whether you are looking to start organizing your data or trying to decide which metrics are important to your growth, Sagence has service offerings that span the gap of data profiling, master data management, and data governance to data refinery implementation and analytic services. Sagence can help provide the necessary insights, guidance, and execution to start turning your data assets into valuable insights. Sagence is a specialized management consulting firm designed to help organizations drive their businesses with information and insight through better data and analytics practices.
REFERENCES:
http://www.futuresmag.com/2013/11/25/fcms-speak-out; Joe Guinan, Chairman and CEO of Advantage Futures, when asked about the Dodd-Frank rules and the impact to the FCM business, November 2013.
http://www.finra.org/web/groups/industry/@ip/@reg/@notice/documents/notices/p424786.pdf; Regulatory Notices, December 2013.
http://www.reuters.com/article/2013/10/30/ny-tabb-group-idUSnBw305948a+100+BSW20131030; Leading Futures Commission Merchants in the U.S. Seen Growing Larger Under New OTC Derivatives Market Reform Rules, Says TABB, Reuters, October 2013.
ABOUT SAGENCE
Sagence is a management consulting firm advising clients in information-intensive industries. We specialize in data management and analytics and in the acquisition, evaluation, and development of critical data assets.
LEADERSHIP & ALIGNMENT
DATA MANAGEMENT
Building a data-driven organization starts with the leadership team. Successful data executives have negotiated the right scope, secured the right authority, acquired the right talent, and aligned with the business to develop and execute effective data strategies.
Just as companies deployed global brand standards, architectures, and brand managers, today’s leading firms have implemented global data governance models, designed data architectures, and established data owners. A strong data management foundation that adequately addresses data quality and integrity positions forward-thinking companies to take their enterprise analytics to the next level.
DATA ANALYTICS
DATA ROI
Because raw data lacks context and value, it is important to recognize that the data required to operate a business is not the same as the data required to understand it. Successful companies have been able to move beyond functional operations analysis and are now delivering enterprise-wide insights. What’s more, when data is combined with—or compared against—external information sources, a unique perspective is gained. It’s this insight that is enabling firms to move ahead of the competition.
Business leaders looking for a new management lever to drive value and expand their market capitalization should look no further than their own proprietary data. The combination of competent data leadership, effective data management, and pragmatic enterprise analytics generates measurable results and delivers superior returns.
