Data Maturity in a Social Business and Big Data World

Dion Hinchcliffe, the executive vice-president of strategy at Dachis Group, in his book Social Business By Design, defines social business as the intentional use of social media to drive meaningful, strategic business outcomes. Companies are leveraging social media platforms and data to drive business, inform consumer engagement, and to enhance and expand the company’s analytical capability. Watching Twitter feeds? Check. Monitoring Facebook and Pininterest? Yep.  Building internal collaboration platforms to more tightly integrate your business partners? Of course.

To harness the transformative power that social business and social business analytics promises, companies need to integrate information from multiple data sources. This includes both structured and unstructured data. It is critical then, to have both a strong data governance foundation in place, as well as an infrastructure that can quickly consume, integrate, analyze, and distribute this new information. Incompatible standards and formats of data in different sources can prevent the integration of data and the more sophisticated analytics that create value.

A company’s ability to strongly leverage social media as a social business will be infinitely enhanced by having a strong foundational data and technology infrastructure, along with data governance policies and processes for integrating social media data sets.

The figure below overlays Hinchcliffe’s social business maturity model (in red, with four of eight community management competencies shown in gold) with a traditional data governance maturity model (shown in blue) and technology maturity model (in orange).

DM Maturity in a Social Business

Implementing cross-channel customer engagement or enriching in-house data with purchased behavioral/lifestyle data WITHOUT already having master data and a master data management system in place would require hours of manual manipulation on the part of employees, leaving little time for the actual analysis of data. Additionally, services such as alerts and recommendations would not be accurately possible (thus potentially risking a privacy violation) without a master profile of the customer. Likewise, an organization’s internal infrastructure (beyond big data clusters) must also be sophisticated enough to move data throughout the organization, when and where it’s needed.

While the rush to social business and big data certainly is on, smart data companies are also investing in foundational data management, data governance, and technology architecture to support their long-term vision.

How Data Management and Data Governance Support Big Data ROI

According to Aberdeen Group (source: Data Management for BI: Fueling the Analytical Engine with High-Octane Information), best-in-class companies take 12 days on average to integrate new data sources into their analytical systems; industry average companies take 60 days; and, laggards 143 days. If you’re an average company (raise your hand if you think you are), it will take you up to 2 months to integrate new data. Do you have that much time? Can you afford to react that slowly to customer or market opportunities? For laggard companies, this really means they’re dead in the water.

Traditional legacy, siloed systems are heavy: incompatible standards and data formats slow or prevents quick integration of existing or existing with new data. This inability to quickly and effectively integrate new data sets for either real-time or predictive analytics limits the ability of organizations to pursue new opportunities, support customer needs, and to drive insights. In short, it limits an organization’s ability to be proactive in revenue-generating situations.

Part of the reason for this time delay and inability to drive the ‘last-mile’ analytics that companies need, is the lack of data management processes. No one needs to explain anymore the myriad of data across organizations and ecosystems: transactional data, operational data, analytical repositories, social media data, mobile device data, sensor data, and structured and unstructured data. Usually a lack of data (with some exceptions) is not the main problem for an organization.

The main problem is how to manage, integrate it, and distribute it (appropriately) so that organizations can nimbly and agilely exploit data and opportunities. The Data Management Association International (DAMA) provides a framework that is a holistic approach to understanding the data and information needs of the enterprise and its stakeholders. Most of my readers already have an awareness of the DAMA ‘wheel’, which highlights ten areas of data management, with data governance as the center point. In addition to the Data Warehousing & Business Intelligence Management pieces of the wheel, other key areas of data management that are important to data integration, analytics and optimization include: data governance, data architecture, master data management, meta data management, and data security.

From a big data perspective, these area help provide answers to the following questions:

  • What data do we have? Where are gaps in data that we need?
  • What data is intellectual property for us that can help us exploit new opportunities?
  • How do we integrate the right data together?
  • How do these data sets relate to each other?
  • Do we have all of the data about this (fill in the blank – person, event, thing, etc.)?
  • What are the permissible purposes of the data? Can we link and leverage these disparate data sets together and still be in regulatory compliance?

Data driven, data centric organizations (best-in-class) consider data needs early and often in the business strategy process. They are not in reactive mode after IT has architected and implemented a solution to then determine what the reporting, analytics, and big data opportunities may be.  Understanding the business strategy and business needs drives strong data management and data governance. Data management and data governance allow the strong management of data assets, so those assets are leverageable for big data purposes in ways that optimizes benefit and return on invest to the organization.

Data management maturity supports big data maturity by providing the policies, processes, and infrastructure to quickly consume, integrate, analyze, and distribute high quality, trusted data to the user’s (employee, executive, customer, business partner) point of touch so that insights can be derived and action taken as rapidly as possible.

Applying Entrepreneurial Principles to Data Governance

I recently read a wonderful article by Daniel Isenberg in the Harvard Business Review article entitled “Planting Entrepreneurial Innovation in Inner Cities” (June 5, 2012).  While on the surface it had nothing to do with data governance, it hit me as I got further in to it, that from an organizational perspective, there are many similarities between new and young data governance efforts and entrepreneurial ventures.

Many data governance efforts start out as entrepreneurial feats, engineered by a few people with a vision, creating it on a shoe-string budget.  Perhaps they have an “angel” investor – an executive sponsor with enough vision to provide some capital and a few people to see what can happen. The goal of course, is to get enough wins (customers) under their belt to build a business plan and take it to an internal “venture capitalist” for full funding, more resources, and to support expansion. The data governance teams have similar qualities to entrepreneurs in terms of the amount of time, energy, creativity and dedication to their vision it takes to build the program out.

So, here is a synopsis of Isenberg’s major principles about fostering an environment of entrepreneurship. Think about how these can be applied in your organization.

Develop an inclusive vision of high growth entrepreneurship-, “It is a reality that a small number of extraordinary entrepreneurial successes have a disproportionately stimulating effect on the environment for entrepreneurship… But, counter this with a strong message to entrepreneurs that they need to play a role in community building. …you need to tirelessly communicate a coherent message to all of the stakeholders and residents, highlighting the entrepreneurial benefits…”

The application of this to your data governance effort is pretty straightforward. Find and nurture relationships with those who are most excited about the business value of data governance and can create the most impact. But, make sure they understand that they need to support and foster data governance through community building and sharing what they’ve learned with others.  The big difference with data governance efforts versus entrepreneurship, is that as data governance efforts across an organization expand and mature, everyone should win, not just a few.

Use best processes, not best practices – “We are a ‘platform’ not a program. An ecosystem exists in nature when numerous species of flora and fauna interact in a dynamic, self-adjusting balancing act. You need to provide a broad platform to support the inclusive vision, for all to interact with each other in innovative ways. Best processes are more important than best practices. One element of “best process” in fostering entrepreneurship ecosystems is experimentation. Experiment. Test. Invent. “

Through collaboration and community-building efforts, data governance efforts continue to build out the platform and portfolio of best process language, products and services that enable the organization’s data ecosystem to thrive and innovate. And, don’t be afraid to try out new things and see if they work. Processes, standards, definitions, policies – these can all be tweaked over time if necessary.

Define principles, not clusters – “Innovation, creativity, design, sustainability, experimentation, entrepreneurship, inclusiveness: these are example principles to be infused into the city’s collective consciousness.  It is the entrepreneur’s job, not City Hall’s or that of a consulting firm, to learn how to identify opportunity, usually where most people think it doesn’t exist. Many of the great opportunities defy definition and lie in the creative “inter-sectors”: health care and the environment; real estate development and information technology and cleantech; education and mobile communications.”

Classically, this is why data governance and data management are based on enterprise architecture and take an enterprise view of data. Trying to solve data issues in silos or divisions can move the ball forward – usually in terms of efficiencies. But to truly be innovative, connections and unlikely combinations across silos, divisions, and even ecosystems need to occur. Visibility into all enterprise data assets, identifying authoritative data sources, and providing high quality data: these are some examples of data governance principles that can support innovation in an organization.

Invest time, not money: “Nothing is free… [but]better to spend your energy persuading the stakeholders that it is worth their while to make those investments…investment is seen as enlightened self-interest.”

Yes, data governance and data management takes time AND money. But, the fact is that you do want all enterprise stakeholder invested in the outcome and success of the program – because they are dependent on quality data to succeed. If they all chip in and have a stake in the game, they will be more interested in helping you succeed.

Fight the battle for talent, not capital: “Make your city an amazing place for the most talented entrepreneurs, innovators and creative people to come to seek their fortunes, to live, work, and play in.”

Data governance isn’t exactly a city, but the concepts of community-building still apply. Set up internal communities using social media to allow folks to come together virtually and share ideas. Spend time trumpeting successes and encouraging the cross-pollination of ideas. Set up your program so it enables success and innovation in the organization by tying it in with key strategic initiatives that have employees talking.

One final point. Isenberg article specifically discussed entrepreneurship in inner cities. He described “Inner city” in this way – “remember just a decade ago when the term ‘inner city’ basically meant ‘dead city’, conjuring up images of destruction, dereliction and despair? Today, inner cities are “in” – innovative, hip hotbeds of convenient culture, commerce and connection.”

Sounds a lot like the world of data to me.

CDO Insights – Starting an Enterprise Data Program from Scratch

In 2009, I became the Chief Data Officer of the State of Colorado, the first for a state in the country. It was a tremendous opportunity, as well as an honor, to be appointed by a governor – and supported by a legislature – who truly had the vision and understood the role of data in an organization to truly transform service delivery and performance management across an enterprise.

There were two primary challenges in creating this role in the enterprise. The first was the development of a strong operational model for the role. What is the span of authority a Chief Data Officer (CDO) should have, both strategically and tactically? How does this authority get created and embedded, via policy, budget, and operations? How and with whom will this role engage across enterprise lines of business (in this case, the executive branch agencies, the legislature, and key stakeholders at the state and local level)? What kind of team is needed to support the CDO?

The second challenge was that the State literally had no history of enterprise architecture or data management principles and policies.  Creating value quickly to both build momentum and to increase support among the skeptics would be critical. There was an abundance of opportunities and work to be done, which I will discuss in a later post.

The Chief Data Officer role can be a crucial part of the C-level, strategic thinking of an enterprise in the era of all things digital and data. It’s been said ad naseum that data and information are some of the most important assets that organizations – private and public sector, large and small businesses alike – have. And of course, it’s true. However, it’s been my observation that most organizations still very much struggle with their level of sophistication around how to really manage, integrate, and leverage this major asset class in a way that drives opportunity, transformation, bottom line results, stock price increase, or improvements in service delivery.  It’s surprising there’s not been more momentum to create this role within organizations.

A strong enterprise information management program can result in the following benefits to organizations:

  • Customer-centric integrated information environment
  • Access to robust information and delivery of that information where needed, including to mobile devices
  • Economies of scale and reduced development efforts and operational costs
  • Consistent and reliable information, with the ability to layer on strong advanced analytics
  • More agile and proactive business operations
  • Platform scalability with more shared services
  • Data as a service, capturing data once and leveraging it across multiple business processes and applications
  • Trust framework that enables appropriate information sharing and access while ensuring privacy, confidentiality, and compliance

An obvious question is: shouldn’t this be what the Chief Information Officer (CIO) should do? Perhaps, but the reality in most organizations is that the CIO is focused on the technology and operations that support the organizational data needs. This by itself is an enormous challenge. Most CIOs are very good consultative partners with regards to how technology can support business operations.However, the true ownership and stewardship of data and information rests on the business side of the house, not with the technologists.

Therefore, the executive suite needs someone who can oversee the strategic business application of its information assets enterprise-wide. Someone who advocates for information; who can facilitate cross-departmental discussions about information; who’s responsibility it is to optimize existing information assets, to identify information gaps, and to work with units to acquire needed data (structured and unstructured); someone who build the trust and partnerships across the organization (chief diplomatic officer? – more on this in a future post); and, someone who can set organizational standards and policies for enterprise information management to improve quality, accuracy, and usability of critical core data assets. These are at the center of a CDO’s responsibilities.

I think that over the next decade, we will see much great interest in and a maturing of the role of the Chief Data Officer in the same way we’ve seen the Chief Information Officer, Chief Strategy Officer, or Chief Information Security Officer roles mature.