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How can IT regain the confidence of the Business to engage early and enthusiastically on the Big Data journey?

I love EMC World (though I can't say the same about Las Vega$). I get an opportunity to talk to customers who are at the dirty and grimy frontline of trying to derive value from all of this Big Data hoopla. They teach me tons!

One theme that came up several times in our conversations was the following:

"I can't get the Business to engage in an envisioning type of engagement. We have lost their trust. So we are forced to start our Big Data journey from the technology."

This is a huge problem. It is hard to drive meaningful business impact and a compelling Return on Investment (ROI) by starting with the technology and hoping that someone in the business finds something of value in the technology. This "Field of Dreams" ("If you build it, they will come") approach is not a good way to run a business.

So what can IT do to build trust with the Business? How can IT regain the confidence of the Business to engage early and enthusiastically on the Big Data journey?

Let's review some simple but actionable recommendations.

Recommendation #1: Architect with Business Outcomes in Mind
Every big data journey should start with a targeted business initiative or business process in mind. If you don't know what problem or opportunity you are trying to address with data and analytics, then to quote Yogi Berra:

"If you don't know where you are going, you'll end up someplace else."

The selection of the targeted business problem or opportunity MUST be done with the Business; it simply is not sufficient for IT to guess or think that they know where best to apply data and analytics for business value. IT organizations need to start the Big Data journey by educating the Business users on how they can leverage data and analytics to power their key business processes; to optimize key business processes, uncover new monetization opportunities and create a more compelling, more profitable customer engagement. This education and awareness process can take a long time, and there are many excellent articles, stories and books (um...maybe something like "Big Data MBA: Driving Business Strategies with Data Science"... just saying...) that can help to accelerate their learning process

Once you have agreement with the Business upon which business initiative or business process to target, then you are prepared to break down that initiative or process into its relevant business requirements: key stakeholders, desired business outcomes (business use cases with supporting decisions), illustrative analytics, brainstormed and prioritized data sources, and prioritized use cases (see Figure 1).

Figure 1: Architecting With Business Outcomes In Mind

Figure 1: Architecting With Business Outcomes In Mind

This Big Data Vision Workshop engagement must start with the business users' involvement on day one, and get everyone prepared for the envisioning work. But sometimes it is hard to find someone on the business that is willing to make that investment; that they simple do not trust IT to stay focused on delivering business outcomes. In that case...

Recommendation #2: Find a (Business) Friendly
It is very tempting to focus your business recruiting efforts with the high visibility business functions such as Marketing, Finance, or Sales. But those groups have historically been saturated with over-promised and under-delivered Business Intelligence and data warehousing promises. So maybe it's better to recruit someone who historically might be underappreciated, like Logistics or Inventory Management or Facilities Management or Customer Service.

It is usually more exciting to focus on the business areas that are trying to "make me more money." But if you cannot find a friendly in that part of the business, then be prepared to focus on those organizations that are more focused on the "save me more money" aspects of the business. Even then, there are countless opportunities to help those organizations to leverage data and analytics to optimize key business processes, such as:

  • Reducing obsolete and excessive inventory
  • Reducing bad debt or bad loans
  • Improving customer retention
  • Reducing distribution costs
  • Optimizing replacement parts logistics
  • Reducing underwriting costs
  • Eliminating unnecessary health care procedures
  • Reducing hospital readmissions
  • Reducing shrinkage
  • And many more.

Recommendation #3: Deliver On The Promise
One thing that I hope we learned from the Business Intelligence and data warehouse days is that it is insufficient to just provide the technology to the users and "hope" that they find something of value. "Hope" is only a strategy if you're in the cosmetics business and doesn't it play so well in the data and analytics space.

Consequently, IT must be prepared to take the business ideas and prioritized use cases coming out of the Big Data Vision Workshop process all the way through the Proof of Value and Operationalization stages (see Figure 2).

Figure 2: Proof of Value and Operationalization Stages

Figure 2: Proof of Value and Operationalization Stages

The Proof of Value stage is designed to prove to the Business that the data science work can produce the necessary analytic lift, or business process improvement, that drives the compelling financial Return on Investment (ROI).

The Operationalization stage operationalizes the analytic and business results of the Proof of Value stage, including:

  • Automating data feeds
  • Automating data transformation and enrichment routines
  • Automating analytic model execution
  • Developing API's to feed analytic results to operational and management systems
  • Hardening analytic models to run more efficiently (in-memory, in-database)
  • Developing any new supporting apps (web-based, mobile)
  • Modifying existing apps to ingest analytic results (dashboards, entry systems)
  • Implementing data governance policies and procedures
  • Implementing security
  • Implementing privacy policies and procedures

Yep, all the hard and ugly IT work necessary to ensure that the Business realizes the financial payback and projected ROI on an ongoing basis; that the results of your big data engagement were not just a one time gig.

Recommendation #4: Prove Yourself Time And Time Again
Now that you've done it once, let's do it again. Be prepared to "wash rinse repeat" time and time again; be prepared to prove yourself and the business value of IT to the Business time and time again by embracing the Big Data Engagement Lifecycle (see Figure 3).

Figure 3: Big Data Engagement Lifecycle

Figure 3: Big Data Engagement Lifecycle

Any one can get lucky once, but in order to continue to build trust and confidence with the Business, we need a business-centric process that allows us to leverage data and analytics to deliver compelling business value time and time again. Not only will IT build trust with the Business in this manner, but IT will also become indispensable to the very success of the Business. Yeah, that's a nice place to be.

Summary: Fight To Be Relevant To The Business
IT needs to resist the urge to take the "Field of Dreams" approach and lead the journey with technology. Do not believe for a moment that if you build the data lake and populate it with some data, that some how, some way, some one will find something of value in that data lake (see Figure 4).

Figure 4:

Figure 4: "Field of Dreams" Big Data Approach

We have moved beyond this approach. We have learned the hard way that the "Field of Dreams" approach does not work. To quote a recent Forbes article titled "Inside American Express' Big Data Journey":

"What are the lessons for traditional businesses looking to Big Data to transform their businesses? ...as [American Express] illustrates, sponsorship from the top remains essential. Long-term journeys require staying power and commitment."

It is not sufficient to just have the Business in the boat with IT; the Business must be standing at the front of the boat hand-in-hand with IT to get the most business value out of your Big Data journey. That means addressing the trust issue right up front. Hopefully this blog has given you some ideas how to enlist the Business to join you on this most excellent Big Data adventure!

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business”, is responsible for setting the strategy and defining the Big Data service line offerings and capabilities for the EMC Global Services organization. As part of Bill’s CTO charter, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, avid blogger and is a frequent speaker on the use of Big Data and advanced analytics to power organization’s key business initiatives. He also teaches the “Big Data MBA” at the University of San Francisco School of Management.

Bill has nearly three decades of experience in data warehousing, BI and analytics. Bill authored EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the Vice President of Advertiser Analytics at Yahoo and the Vice President of Analytic Applications at Business Objects.

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