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.

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Data and Trust – Thoughts from the World Economic Forum’s Global Agenda Outlook 2013

In the recently released “Global Agenda Outlook 2013” by the World Economic Forum, one of the main topics that is tackled as part of the ‘agenda’ is titled ‘Thriving in a Hyperconnected World”.  The main premise is that the physical world and the digital world are merging rapidly, and institutions and leaders are not prepared to deal with it. Not only are the technologies evolving, but the amount of data being generated is completely unprecedented, yet will only grow.

Two of the major components of this “hyperconnectedness” that the WEF discusses are data and trust. Marc Davis with Microsoft Online Service Division frames it nicely: “[Big data] is a question of the structure of the digital society and digital economy, what it means to be a person, who has what rights to see and use what information, and for what purposes might they use it.”

Globally, countries and industries are dealing with the policy, economic and regulatory structures (on top of the technical interoperability challenges) to control the flow and sharing of data, particularly personal data. Yet there is virtually nothing that is done today without a data component. There are both huge societal benefits to the amount of data generated today, as well as potentially enormous – and life-threatening- drawbacks to this data if not managed properly, if collected erroneously, and if inappropriately shared.

There are many reasons why we each give data up – to open a bank account, to purchase a vehicle, to get healthcare treatment, to find people to date, to unlock a badge from our favorite gaming site. But in these instances, we make a conscious  choice to give up certain pieces of data and information about ourselves.

But we don’t know what the internal data quality practices are of the companies to whom we give data; we don’t know how they manage their cyber security practices; we don’t know how their internal access and authentication controls are managed; we don’t know if the company has the ability to do tagging at the data element level to fortify its privacy compliance protocols; we don’t know to whom the company resells our data; we don’t know if the company’s legitimate business partners with legitimate access to our data are also protecting our data with the same degree of integrity.

Knowing what I know about the actual limited capabilities of federal and states governments here in the U.S. to actually integrate and share data, I’m far less concerned with ‘Big Brother’ than I am with Amazon and Apple (both of whom seem to do a far more effective and efficient job of managing my data correctly) doing something creepy with my data (like recommend me purchasing a Justin Bieber CD).

Trust frameworks, transparency, policies, accountabilities – these are all steps on the right path to building trust. To engender trust by people, by society, in how data is collected, managed, and used, requires multiple degrees of sophistication far beyond where many organizations and institutions are today. This includes with technology, policies and regulations, and economic models. Unfortunately, policy will never keep up with the speed of technology innovation, so it may take awhile to get to trust.

Most importantly, however, individuals need to take responsibility for their data: being educated about their data, about how to control it, and to be given more controls over their data (especially when its in the hands of institutions). This part of the discussion is largely absent from the overall debate, and needs to be given its due attention.

Thoughts about how to move this individual responsibility discussion forward?

Identity, Data, Privacy and Security – Tumbling Together

For over a decade, the Federal Government has had numerous efforts and initiatives on identity and access management (IAM). These efforts morphed into identity, credential, and access management (with of course its own acronym, ICAM), underscoring a fundamental principle of … Continue reading

Beyond Quality And Security – The Importance Of Establishing Control Points For Information Management Across The Organization

Strong data management doesn’t just begin on the back end, when the data actually hits a database. It begins long before that, early in the data lifecycle, and across many areas of the organization.

One of the crucial elements in becoming a data-centric organization is in culturally changing the awareness of thinking about data from a variety of prisms. Strategies come down from top management; specific goals and objectives then get developed. The next questions should be: what data do we need to support those goals, objectives, programs etc? Does that data already exist in the organization or do we have a gap? For the gaps, how do we close them and how do we ensure tightness and alignment with existing data management strategies?

There are a number of control points that come out of this scenario:

  • Strategic planning – What data do we need to measure success?
  • Goals and objectives – What KPIs and metrics are important? What type of reporting and dashboards are required? Do we have all the data that we need for reporting and metrics measurements? Do we trust in the quality and integrity of the data that we need for reporting? If not, what gaps do we need to close to build trust?
  • Budgeting and financing – What controls have we implemented to support the optimization of our data investments across the entire enterprise? Are we aligning various programs across the organization such that we reduce data silos and redundancy, and optimize information sharing and infrastructure development where possible? Does someone have stated authority and responsibility for overseeing this planning and budgeting?
  • Business case development – What data do we have in-house (presumes a knowledge of all enterprise data assets) to support new programs or applications? Can we leverage these in-house data sets for this purpose (compliance/regulatory check-point)? How do we close the data gaps we have (can we capture data via existing sources? Do we need to purchase data from 3rd parties? Are there open data sets that are leverageable?)
  • Requirements gathering – Where are the authoritative sources of the different data sets we need? Are we leveraging organizational reference and master data?
  • Build vs. buy decisioning – If we build something in-house, how can we maximize previous infrastructure investments in data, hardware, middleware, and exchange mechanisms so as to minimize duplication or silo building? Buying a solution means building in checkpoints for ensuring ease of integration and data extraction.
  • Contracts and Procurement – What language do we have in our contracts to enforce compliance or alignment with internal data management and data security policies? Do we always get a data dictionary? Do we ask vendors to provide us mapping to our conceptual and logical data models? Do we ensure data quality levels (for certain types of acquisitions)? Who actually owns the data? If we’re outsourcing our data, what are our access rights for transactional, analytical, regulatory, and recovery purposes?

Organizations that think this way are truly data-centric organizations. Not only do they understand data as an asset, but also both try to protect it from dilution and look for the multiplier effect on their data investment by improving the leveragability of data across the organization and its ecosystem.

Davos and Data – Please Don’t Forget the Basics!!

The annual World Economic Forum recently ended in Davos (one year, I WILL get an invitation to attend this!!).  Those of you who follow my Twitter feed know that data was a big topic at the WEF this year. There were several sessions on the topic, and a report titled “Big Data, Big Impact: New Possibilities for International Development” was released.  The report focuses specifically on the impact the collection and proper application of big data (particularly from mobile devices) can have on financial services, education, agriculture and health care.

Yesterday, the WEF’s Global Agenda Council on Emerging Technologies released its list of top 10 emerging technologies for 2012.  Number one on that list is Informatics for adding value to information, which the Council further explained as:

“The quantity of information now available to individuals and organizations is unprecedented in human history, and the rate of information generation continues to grow exponentially. Yet, the sheer volume of information is in danger of creating more noise than value, and as a result limiting its effective use. Innovations in how information is organized, mined and processed hold the key to filtering out the noise and using the growing wealth of global information to address emerging challenges.”

Informatics beat out some very cool scientific areas such as synthetic biology, nanoscale design of materials, and high energy density power systems. Data has gone mainstream.

In everyone’s rush to jump on the ‘big data’ bandwagon, the ‘informatics’ bandwagon, the ‘unstructured data’ bandwagon, there are foundational items that need to addressed if organizations are going to see the kinds of payoffs they should be having, or if this becomes added to the list of trendy things that didn’t work out.

#1 – Have a plan. An enterprise information management strategy is absolutely necessary. Your business has a strategic plan (hopefully). There is no way any business today can operate or innovate without using and leveraging data, so there should be a plan around the capture, usage, maintenance, distribution, security, and disposition of your corporate data assets.

#2 – Someone should have ultimate responsibility and authority for data. This is not the CIO. This is not the CTO. This is not the IT team. This is someone who is charged with the responsibility of managing data from the enterprise perspective who represents the business, who sits on the executive leadership team, who makes the executive decisions, and who’s ass is on the line for the overall quality, integrity, and optimization of those data assets.

#3 – There must be an investment in data. This investment should be in the form of people, dollars, training, and technology.

If the foundational items aren’t done, what your company will have is still a bunch of siloed data of questionable quality – you’ll just have more of it.

Starting an Enterprise Data Program From Scratch, Part 2

In my initial blog on December 11, I kicked off dataTrending with a discussion on building an enterprise information management (EIM) program from scratch, as well as what the role of the Chief Data Officer (CDO) should be. The two big challenges we faced at the State of Colorado with regards to EIM were, first, the operational authority and scope of the CDO and program. This blog picks up where the first blog left off, and deals with the second challenge, how to build value quickly in an organization with no history or reference point for this type of work.

The State literally had no history of enterprise architecture or data management principles and policies beyond individual agencies.  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. How should we start and how do we prioritize opportunities? How do we organize the work and develop a framework that is repeatable and sustainable across a $19B organization? How do we manage work across multiple swim lanes – governance, policy and process, change management, technology and tools – at once? How does one mobilize an organization to start thinking and acting differently about its data? The cultural and trust issues can be real impediments to success and need to be addressed both head on and with diplomacy.

The most critical factor was to align and prioritize this work with the strategic needs, opportunities, and key business drivers of the State. What was important from the executive management’s (the Governor, Legislature, and Agency Directors) perspective? What were their top problems and issues they were trying to solve that could be supported by our program? This is the only way, in my opinion, an EIM program can truly add value and keep support.

The three prisms through which we approached our work was legislative, governance, and operations.

Through a series of four laws over two years, we established the state’s intent around data sharing and information management; explicitly gave agencies permission to share data (unless a federal or other state law expressly prohibited sharing of certain data); and, established a governance board that would retain continuity through administration changes.

On the governance side, we purposefully asked for a mix of business, technology, and financial representatives from the agency participants to ensure that business was driving priorities. Meetings were held monthly, and a very actionable set of deliverables was developed and worked through so progress could be quickly seen.

Operationally, we adopted enterprise architecture and data management frameworks to ensure our approach had a roadmap and followed industry best practices. We created an enterprise data strategy and work priorities were developed through input from key stakeholders across the organization and the governance board.

One of our primary business drivers was enterprise information sharing to inform policy making, resource decisioning, and program management. We identified three primary communities of interest with major information sharing initiatives across the state agencies and leveraged those projects to begin building out our portfolio of policies, processes, procedures, technologies, and tools that we would need to support enterprise initiatives. Into the project budgets we added line items for key tasks, activities, or people to support the work. Things like an enterprise architecture tool, business analysts, and data systems inventorying.

Instead of trying to tackle the entire data management framework, we identified four major areas that would support the information sharing business driver to begin our policy and standards work. And then finally, we just dove in and started the hard work. It was a major effort of entrepreneurship in a government environment. It was not always easy, and not everything we did was a huge success. But we had executive support and were iteratively able to make progress and show small wins built into bigger successes.