Monday, 21 July 2014

Data-driven entrepreneurs



Leading a business intelligence project can be like bootstrapping a startup. Doing it well demands the mindset of a data-driven entrepreneur, along with BI tools that leverage open data and enable an agile approach.

Data-driven entrepreneurs are individuals responsible for successfully applying their resources to transform data into actionable insights and profitable opportunities. They may be analysts, managers, or executives who work in an established organisation or are independently in charge of a startup. Their goal remains the same either way: to persuasively propagate the power of data into the lives of others, so that decisive actions can be taken quickly to grow incomereduce risk and save money.

Open Data Opportunities

Data-driven entrepreneurs are always on the look out for new sources of Open Data that can then be re-purposed for profit. In fact, local and central governments are expecting these entrepreneurs to find ways to unlock the economic value of data by applying advanced analytics and coming up with innovative ways of using the information. This release of public data has been called "a new economic stimulus tool for cities" in the Wall Street Journal:
More cities are putting information on everything from street-cleaning schedules to police-response times and restaurant inspection reports in the public domain, in the hope that people will find a way to make money off the data. 
Research from McKinsey & Co has noted that the continued release of Open Data has the potential to generate more than $3 trillion a year across seven sectors. They note several benefits that data-driven entrepreneurs have begun to deliver, such as the segmentation of new markets, new product and service opportunities, and overall improvements in the effectiveness of operations. McKinsey goes on to note:
"Open data can become an instrument for breaking down information gaps across industries, allowing companies to share benchmarks and spread best practices that raise productivity. Blended with proprietary data sets, it can propel innovation and help organizations replace traditional and intuitive decision-making approaches with data-driven ones. Open-data analytics can also help uncover consumer preferences, allowing companies to improve new products and to uncover anomalies and needless variations. That can lead to leaner, more reliable processes."

An entrepreneurial effort

When it comes to undertaking a Business Intelligence (BI) pilot project, having the will of an entrepreneur is essential. Overcoming the risk of implementation failure is a priority: consider the 70-80% failure rate noted in 2011 by Gartner. Is it any wonder that on-premises and in-house projects are worrisome, especially when the tools have yet to be tested and experienced BI specialists aren't on-hand to help decision-makers overcome misconceptions.

Data-driven entrepreneurs must be able to experiment when there is insufficient data to justify their decisions, especially those decision cost money. BI tools may or may not meet the requirements of everybody who needs readily accessible and consumable data to experiment with. This challenge, as Brad Peters, CEO and Co-Founder of Birst, recently cautioned in a webcast, is simply due to the fact that "one size-fits all is hard to do"...
"You have to take a toolset and wrap that around in individual’s business problem and deliver them the answers to their questions. You have to deliver their metrics. You have to deliver against their business strategy. And everybody’s is different."
It takes entrepreneurial initiative to figure out how to best meet the requirements of everybody who needs access to business intelligence, so when there is a means to test-drive BI-as-a-Service in the Cloud with the agility of a startup, that's the smartest way to gain the feedback needed to avoid unnecessary risk of failure.

Build-Measure-Learn

Eric Ries, the entrepreneur and author who pioneered the Lean Startup movement, warns us in an HBR article about vanity metrics: "numbers which look good on paper but aren’t action oriented". He stresses the value of establishing baseline metrics around your minimum viable product (MVP) in order to gauge the feedback from early adopters so that improvements can be made and measured. He notes that we all agree that: "better analytics leads to better results", to which I would add that effective usage of BI should measure the evidence of its impact, whether it be income growth, cost savings, or increased customer satisfaction.

Ries tell us that "the fundamental activity of a startup is to turn ideas into products, measure how customers respond, and then learn whether to pivot or persevere." The Lean Startup technique is said to follow this sort of Build-Measure-Learn feedback cycle which begins with a hypothesis, followed by the design of a minimal viable product (MVP). This equally applies to BI projects.

Paul McManus of Boston University's School of Management has been exploring entrepreneurship in the digital economy on his blog. He writes about the fact that "the Internet had fractured the meaning of the word product" because it is now possible to "build a great business around a simple service." He goes on to explain that entrepreneurs have begun making devices that connect to the Internet whereby a flow of usage data can be fed back to their owners so that entrepreneurs can offer value-added service for data.

Harnessing the power of feedback in more ways than one is how these entrepreneurs improve their products, as well as services. The connected devices of the Internet of Things will continue to spur new developments in Cloud-based data services that can circulate feedback from sensors to BI dashboards for the users, as well as the entrepreneurs who act as their partners in continuous improvement. By being lean and agile, they learn and adapt together, responding to evidence of what is working, and what isn't.

The Agile path

In an article for TDWI, Ken Collier discusses his expertise in agile Business Intelligence and Data Warehousing in relation to Reis' own feedback cycle (Build-Measure-Learn), and it is not all that different from building a minimal viable product (MVP), for example, a disposable prototype report or dashboard mockup is designed which, he explains, is populated with the snapshot data that meets the business user requirements. He suggests that the leaders of BI projects are not all that different from data-driven entrepreneurs who have correctly discovered what customers want. It's a case of using "agile BI techniques to build, refine, and mature the solution."

Collier is a true proponent of what he calls "agile analytics" and has written a book on the subject of this style of project, which is for those preparing to undertake an extensive BI project. He shares three consistent truths that he experienced during work as a business intelligence consultant:
"Building successful DW/BI systems is hard; DW/BI development projects fail very often; and it is better to fail fast and adapt than to fail late after the budget is spent."
But what if it were less risky and easier to learn what you need to know about the analytics solution you require?

Bridging the gap

Data-driven entrepreneurs are beginning to bridge the gap between the legacy purchasing methods from the enterprise software era. David Pidsley, Data Strategist at Cause Analytics, highlights the status quo is still often tendering processes with lengthy requests for tenders (RfTs) being defined upfront by buyers before they have undertaken the trial-and-error process. He sees this transitioning into the 'MVP BI approach' described above. Cause Analytics allows users to pick'n'mix their minimal feature set with a pay-as-you-go service that begins with a free trial - giving their prospective customers very limited downside, in terms of time/cost spent or technological risk, and an unlimited upside if BI is deployed.

We believe that data-driven entrepreneurs will take hold of the opportunity to test-drive and customise their BI dashboards. Mitigating the risk of adopting costly and complicated  business intelligence software is the wise thing to do. In-house IT teams usually have their hands full and should not be expected to become experts in BI and analytics overnight. When it comes to making sense of data for critical decision making, time is of the essence. Better to shoot for an MVP than miss the target completely by undertaking a RfT.
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