Even though Business Intelligence applications have been out there for decades lots of people still struggle with “how do I get started with BI”. I constantly deal with clients who mistakenly start their BI journey by selecting a BI platform or not thinking about the data architecture. I know it’s a HUGE oversimplification but in a nutshell here’s a simple roadmap (for a more complete roadmap please see the Roadmap document in Forrester BI Playbook) that will ensure that your BI strategy is aligned with your business strategy and you will hit the road running. The best way to start, IMHO, is from the performance management point of view:
Catalog your organization business units and departments
For each business unit /department ask questions about their business strategy and objectives
Then ask about what goals do they set for themselves in order achieve the objectives
Next ask what metrics and indicators do they use to track where they are against their goals and objectives. Good rule of thumb: no business area, department needs to track more than 20 to 30 metrics. More than that is unmanageable.
Then ask questions how they would like to slice/dice these metrics (by time period, by region, by business unit, by customer segment, etc)
Business intelligence has gone through multiple iterations in the past few decades. While BI's evolution has addressed some of the technology and process shortcomings of the earlier management information systems, BI teams still face challenges. Enterprises are transforming only 40% of their structured data and 31% of their unstructured data into information and insights. In addition, 63% of organizations still use spreadsheet-based applications for more than half of their decisions. Many earlier and current enterprise BI deployments:
Have hit the limits of scalability.
Struggle to address rapid changes in customer and regulatory requirements.
Fail to break through waterfall's design limitations.
Suffer from mismatched business and technology priorities and languages.
Beware of insights! Real danger lurks behind the promise of big data to bring more data to more people faster, better, and cheaper: Insights are only as good as how people interpret the information presented to them. When looking at a stock chart, you can't even answer the simplest question — "Is the latest stock price move good or bad for my portfolio?" — without understanding the context: where you are in your investment journey and whether you're looking to buy or sell. While structured data can provide some context — like checkboxes indicating your income range, investment experience, investment objectives, and risk tolerance levels — unstructured data sources contain several orders of magnitude more context. An email exchange with a financial advisor indicating your experience with a particular investment vehicle, news articles about the market segment heavily represented in your portfolio, and social media posts about companies in which you've invested or plan to invest can all generate much broader and deeper context to better inform your decision to buy or sell.
But defining the context by finding structures, patterns, and meaning in unstructured data is not a simple process. As a result, firms face a gap between data and insights; while they are awash in an abundance of customer and marketing data, they struggle to convert this data into the insights needed to win, serve, and retain customers. In general, Forrester has found that:
The problem is not a lack of data. Most companies have access to plenty of customer feedback surveys, contact center records, mobile tracking data, loyalty program activities, and social media feeds — but, alas, it's not easily available to business leaders to help them make decisions.
There's never been a question on the advantages of open source software. Crowdsourcing, vendor independence, ability to see and in some cases control the source code, and lower costs are just a few benefits of open source software (OSS) and business model. Linux and Apache Hadoop are prime examples of successful OSS projects. It's a different story, however, when it comes to OSS BI. For years, OSS BI vendors struggled with growth because of:
The developer-centric nature of open source projects. The target audience for open source projects is developers, which means deals are mostly sealed by technology management. The industry, on the other hand, has gravitated toward business decision-makers within organizations over the last several years. However, business users are less interested in the opportunities that a collaborative open source community offers, and more concerned about ease of use and quick setup. Indeed, Forrester's research constantly finds evidence correlating business ownership as one of the key success factors for effective BI initiatives.
The battle of trying to apply traditional waterfall software development life-cycle (SDLC) methodology and project management to Business Intelligence (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 of BI strategies, 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 "outputs first" mentality. Those projects employed bottom-up approaches that focused on the program and technology first, leaving clients without the proper outputs that they needed to manage the business. Organizations should use a top-down approach that defines key performance indicators, metrics, and measures that align with the business strategy. They must first stop and determine the population of information required to manage the business and then address technology and data needs.
Between 2012 and 2014, mobile BI adoption shot up: Forrester survey data shows that the percentage of technology decision-makers who make some BI applications available on mobile devices has nearly quadrupled, and the percentage who state that BI is delivered exclusively via mobile devices has risen from 1% in 2012 to 7% in 2014. While this clearly demonstrates that mobile BI is gaining traction, the actual mobile BI adoption picture is rather more nuanced. Our ongoing research and client interactions show that mobile BI adopters fall into three overall groups; some organizations
Really ‘get’ the transformational potential of mobile BI. They are the ones who understand that mobile BI is about much more than liberating reports and dashboards from the desktop. They focus on how data can be leveraged to best effect when in the hands of the right person at the right time. If necessary, they’re prepared to change their business processes accordingly. For those companies, mobile BI is an enabler of strategic goals, and deployment is a journey, not an end in itself.
Make mobile BI available because it’s the right thing to do, or they’ve been asked to. Many of these organizations are reaping considerable benefits from their mobile BI implementations, and the more far-sighted of them are working on how to move from the tactical to the strategic. Equally, many are trying to figure out where to go from here, in particular if the initial deployment doesn't show a clear benefit, let alone return on investment.
To compete in today's global economy, businesses and governments need agility and the ability to adapt quickly to change. And what about internal adoption to roll out enterprise-grade Business Intelligence (BI) applications? BI change is ongoing; often, many things change concurrently. One element that too often takes a back seat is the impact of changes on the organization's people. Prosci, an independent research company focused on organizational change management (OCM), has developed benchmarks that propose five areas in which change management needs to do better. They all involve the people side of change: better engage the sponsor; begin organizational change management early in the change process; get employees engaged in change activities; secure sufficient personnel resources; and better communicate with employees. Because BI is not a single application — and often not even a single platform — we recommend adding a sixth area: visibility into BI usage and performance management of BI itself, aka BI on BI. Forrester recommends keeping these six areas top of mind as your organization prepares for any kind of change.
Some strategic business events, like mergers, are high-risk initiatives involving major changes over two or more years; others, such as restructuring, must be implemented in six months. In the case of BI, some changes might need to happen within a few weeks or even days. All changes will lead to either achieving or failing to achieve a business. There are seven major categories of business and organizational change:
Forrester recently published its 2015 Predictions for Asia Pacific. I wanted to highlight some specific trends around customer insights (CI) and big data, two very hot topics for many AP-based organizations.
We strongly believe that success for many organizations hinges on your ability to close the gap between available data and actionable insight. Marketing is taking the lead here, as CI pros seek to use data to fuel customer engagement improvements. Hence 2015 will be a year of increased fragmentation as reliance on analytics spreads across organizations.
What will this mean for you? More cloud-based and mobile analytics, more demand for interactive and responsive analytics, and more use of specialist and niche BI and analytics service providers. Given this backdrop, Forrester believes that:
Analytics spending will increase by at least 10% across the region. Yes analytics spending will increase, but less of it will be visible in the CIO's budget. Marketing and other business departments will drive analytics investments to address specific challenges and opportunities. The technology management (TM) organization will have little control over the implementation and deployment of niche and specialist BI and analytics services.
At the same time, for business leaders, having access to quality network infrastructure represents a vital underpinning for their digital business and their long-term competitive advantage. We predict that by 2015 and beyond:
The telco business model will shift from sustaining to enabling critical infrastructure. Traditionally, the telco business model focused on sustaining operational efficiency of network infrastructure. In the years ahead, we predict a shift toward enabling solutions that support telco clients to engage with their customers more effectively. This mirrors not only the CIO’s shift from IT towards business technology but will also be the overarching theme during the transformation of the telco business model.
An inquiry call from a digital strategy agency advising a client of theirs on data commercialization generated a lively discussion on strategies for taking data to market. With few best practices out there, the emerging opportunity just might feel like space exploration – going boldly where no man has gone before. The question is increasingly common. "We know we have data that would be of use to others but how do we know? And, which use cases should we pursue?" In It's Time To Take Your Data To Market published earlier this fall, my colleagues and I provided some guideance on identifying and commercializing that "Picasso in the attic." But the ideas around how to go-to-market continue to evolve.
In answer to the inquiry questions asked the other day, my advice was pretty simple: Don’t try to anticipate all possible uses of the data. Get started by making selected data sets available for people to play with, see what it can do, and talk about it to spread the word. However, there are some specific use cases that can kick-start the process.
Look to your existing customers.
The grass is not always greener, and your existing clients might just provide some fertile ground. A couple thoughts on ways your existing customers could use new data sources: