Since the term BI is often used to also include data management processes and technologies, let's assume that in your case you are only looking for expertise required to build reports and dashboards and it does not include
Data integration (ETL, etc) expertise
Data governance (master data management, data quality, etc) expertise
Data modelling (relational and multidimensional) expertise
Over the past few months, following publication of my "Customer Insights Center of Excellence" report , there’s been a significant uptick in questions by insights and analytics teams who want to talk to us about CoEs. That’s a positive sign that firms are feeling the crunch to get more value from their insights functions. What’s the evidence for that conclusion? What can we learn from who’s asking about insights CoEs? And most importantly, what really matters in how you organize?
Before we dig in to answers, let’s set the bar on what “great” looks like in truly customer obsessed organizations: they use data for insights to improve customer experience that matters most to business outcomes. As my colleagues James McCormick, Brian Hopkins, and Ted Schadler write in their recent report, "The Insights-Driven Business," customer obsessed businesses act on insights in closed loops, at speed, and at scale in all parts of the firm. They embed analytics and testing directly into operating teams. And, firms who implement these approaches run faster and fleeter than you. The pressure is on from insights-driven organizations.
Is your business digital? Like Domino’s Pizza, do you realize that you are not a product or service business, rather you are a software and data business that provides products or services? Do you exploit all of your customers' data to know them inside-out? Are customers flocking to you because you are driving every engagement with insight about them? If the answer to any of these questions is not a resounding, “Yes!”, then you are losing revenue and shareholder value.
In Forrester’s new report, The Insight Driven Business, my colleagues Ted Schadler, James McCormick and I identify a type of business that ignores the "data driven" hype. Instead, insights-drivenbusinesses focus on implementing insights - that is actionable knowledge in the context of a process or decision - in the software that drives every aspect of their business. This is a big shift from most firms that fret over big data and technology. Instead insights-driven businesses focus on turning insights into action. The big data and technology pieces come along naturally as a consequence.
To gauge the economic impact of insights-driven businesses, Forrester built a revenue model that conservatively forecasts insights-driven businesses will earn about $400 billion in 2016; however, by 2020 they will be making over $1.2 trillion a year due to an astonishing compound annual growth rate between 27% and 40%. Given that global growth is less than 4%, how will they pull this off? Plain and simple, they’ll do this by understanding customers more deeply and using that insight to steal them from their competition.
Next time you find yourself wading through data points, sifting out patterns from the noise, hoping to catch the rare pearl of insight to affix to your business plan, know that you are not alone. Employees worldwide incessantly engage with data, and the companies they work for urgently execute on data-driven strategies in a race for better, faster results. Data pervades the workplace and continues to grow in terms of volume and variety: Research suggests that by 2020, the number of connected devices will more than triple, tens of thousands of data scientist jobs will be in high demand, and the majority of sales decisions will be data-driven.
But using data regularly doesn’t mean that employees truly understand it – or are comfortable with data practices. Specific obstacles prevent individuals – at the top and bottom of the organization – from eliciting effective insight. Forrester’s Business Technographics® and ConsumerVoices MROC data shows that while individuals rely heavily on data for decision-making, they still grapple with key challenges regarding the accuracy, volume, value, and security of the data they use:
Consumers (and B2B customers) are more and more empowered with mobile devices and cloud-based, all but unlimited access to information about products, services, and prices. Customer stickiness is increasingly difficult to achieve as they demand instant gratification for their ever changing tastes and requirements. Switching product and service providers is now just a matter of clicking a few keys on a mobile phone. Forrester calls this the age of the customer, which elevates business and technology priorities to achieve:
Business agility.Business agility often equals the ability to adopt, react, and succeed in the midst of an unending fountain of customer driven requirements. Agile organizations make decisions differently by embracing a new, more grass-roots-based management approach. Employees down in the trenches, in individual business units, are the ones who are in close touch with customer problems, market shifts, and process inefficiencies. These workers are often in the best position to understand challenges and opportunities and to make decisions to improve the business. It is only when responses to change come from these highly aware and empowered employees, that enterprises become agile, competitive, and successful.
The battle of trying to apply traditional waterfall software development life-cycle (SDLC) methodology and project management to BI has already been fought — and largely lost. These approaches and best practices, which apply to most other enterprise applications, work well in some cases, as with very well-defined and stable BI capabilities like tax or regulatory reporting. Mission-critical, enterprise-grade BI apps can also have a reasonably long shelf life of a year or more. But these best practices do not work for the majority (anecdotally, about three-quarters) of BI initiatives, where requirements change much faster than these traditional approaches can support; by the time a traditional BI application development team rolls out what it thought was a well-designed BI application, it's too late. As a result, BI pros need to move beyond earlier-generation BI support organizations to:
Focus on business outcomes, not just technologies. Earlier-generation BI programs lacked an "outcomes first" mentality. Those programs employed bottom-up approaches that focused on the project management and technology first, leaving clients without the proper outcomes that they needed to manage the business; in other words, they created an insights-to-action gap. BI pros should use a top-down approach that defines key performance indicators, metrics, and measures that support the business' goals and objectives. They must resist the temptation to address technology and data needs before the business requirements.
On October 14, I attended Big Data & Business Insights 2014 in Bangkok — the first public big data event in Thailand. I spoke about how to use big data to increase customer value in the age of the customer — a topic that seemed a bit distant from the audience’s daily reality. Most of them use traditional data warehouse and business intelligence tools and are new to big data solutions like Hadoop platforms, big data visualization, and predictive solutions. Here’s what I came away with:
Big data is still new to Thai businesses. Most big data projects in Thailand are still at the testing stages, and these trials are taking place in university labs rather than commercial environments. Dr. Putchong Uthayopas of the Department of Computer Engineering at Kasetsart University noted that big data projects in Thailand are now moving from pilot projects to actual usage.
Organizations need more details of real big data solutions. Thai businesses have held off investing in big data solutions because they felt uncertainty about the outcomes of big data projects. Attendees showed a lot of interest when I talked about big data usage in traditional industries, such as John Deere’s “Farm Forward” use case, which helped farmers make better decisions on what, when, and how to plant.