For many organizations seeking to transform and enhance their analytics landscape, deciding on the right tools is of secondary importance to a more pressing need in today’s rapidly changing business climate: agility. Companies today handle analytics in two different groups. One is a group focused on the exploration of large volumes of structured and unstructured data to provide insights and predictive analytics, but these groups are not prevalent in all industries, and vary from company to company. Instead, I want to focus on the group that may not always get the glory, but is crucial to giving organizations visibility to their performance: the traditional BI group in a business’s IT organization.
Unlike large e-commerce enterprises that are dealing with petabytes of customer data, the traditional enterprise is instead most apt to develop a BI strategy with the primary purpose of supporting traditional processes around areas such as finance, sales, supply chain, and human resources. To these companies, where performance of their front-office and back-office functions is of top concern, agility is paramount to adapt to the changing needs of the business as far as how it delivers information. This is a pressing challenge that is becoming even more vexing in the wake of an increasing volume of actionable data.
Agility isn’t unearthed based solely on the tools selected. A sturdy foundation must come first, one that allows an organization to assess its resources and current processes, and then fine-tune its BI strategy to focus on what is really important. Incremental changes without a holistic accounting of how each process affects how the business is run and which metrics are most effective often can result in backward progress; companies might have the means to compartmentalize and analyze more data, but that doesn’t mean they know how to make sense of it.
Agility isn’t unearthed based solely on the tools selected; a sturdy foundation must come first.
Companies must first decide how they are going to use analytics to improve overall business effectiveness. Many organizations today are in reactive mode with BI, using metric roadmaps that are old and obsolete. BI teams are focusing too much time on servicing production support or ad hoc report requests rather than building a defined metrics roadmap.
A first step to transforming analytics is to have an honest discussion at the C-level about what metrics drive the company, and how much ad hoc analytics capacity will be required on a regular basis to deal with fluctuations in a company’s business model. A rapidly growing or diversifying company typically needs a much larger ad hoc analytics capability than a company with a steady product portfolio and customer base. Additionally, many companies do not have any one set of metrics they can point to as the “holy grail.” Instead, there is often a proliferation of metrics, many with the same title but displaying different results depending on the line of business producing the metric. At PwC, we typically jumpstart these conversations with industry-specific metrics maps. This gives our clients a baseline to compare their current metrics mix, helps clients envision what their ultimate metrics maturity goals should be, and ultimately forms the bases for a BI strategy and roadmap.
A More Credible BI Organization
Even with a strong BI strategy, delivering credibly on a BI roadmap is not simple. BI projects often fail to meet their primary objectives in the time frame and budget originally envisioned for a number of reasons. First, many teams forget that new BI initiatives are inherently iterative, and require a more incremental project methodology. Second, the availability and quality of data to feed a metric is often assumed to be much higher than it is in reality. Last but not least, the volume and complexity of the data are often underappreciated.
To overcome these challenges, BI delivery organizations need to be more agile. Typically one of the most important exercises PwC helps our clients with is baselining their BI organization and identifying how it can be strengthened. Many times, training and reorganizing a BI organization can lead to large efficiency gains without large BI software or hardware purchases. Successful BI teams work to realize broad metric roadmap initiatives by adopting a more agile approach to project delivery, providing value and reviewing results with the business on a monthly basis, and setting expectations with BI program stakeholders that additional initiatives such as process re-engineering, data cleansing, or the acquisition of more powerful BI technologies may be required to meet the roadmap as more is discovered about the underlying data. Additionally, the extended BI organization should be thought of as a combination of the business, functional owners of key source data systems, and the core BI technical team, and all be made equally accountable for the success of a BI initiative.
BI Organization Skill Mix
Another critical factor in developing a successful BI delivery organization is the skill mix within the core BI team. One thing we’re finding with our clients is that the business analysts who understand the systems and the data are really the ones who understand the limits of what the information can and cannot be used for. Developing senior analysts who have this business knowledge is a sound strategy for companies that wish to become more mature in their analytics function, and certainly for those companies with an end game of completing a full analytics transformation.
Another way to think of it is that the BI team should be split between analytics and technical professionals. Analytics professionals focus on articulating what metrics should drive the business and the overall effectiveness, while technical professionals focus on fulfilling metrics requests.
While this is not a new issue, the rapid pace of advancements in technology and the diverse array of options in the marketplace are bringing it to the forefront and are making analysts even more valuable to the business. Combined with an increased interest in self-service BI, focusing on improving the BI team skill mix gives many organizations a strong starting point for an analytics transformation.
Technology Architecture and Governance Policies
One of the more interesting developments in the last five years is the explosion of new BI tools, both at the database and visualization layers. While this is great for BI software companies, it has been confusing for BI teams that have to sift through and integrate these often disparate technologies. As companies look at these new platforms, they should consider:
- How well do the technologies integrate so that the number of necessary BI technical skill sets in an organization can be minimized?
- How can the architecture and a series of policies be developed so that development and iteration with metric owners can be as efficient as possible without sacrificing the quality and performance of the data and corresponding reports?
Take SAP’s BI and database landscape as an example. In the last seven years, it has gone from using primarily SAP Business Warehouse (SAP BW) for data warehousing and reporting to now combining SAP BW with SAP HANA and Sybase for data warehousing and using a host of solutions, including SAP BusinessObjects BI solutions, SAP Lumira, and SAP Predictive Analytics, for reporting. As the technology improves, integration is a challenge.
One way PwC has helped our clients is to baseline their existing BI capabilities and help build two-to-three-month proofs of concept (POCs) for new analytics architectures. We then help the client make an honest assessment of the improvements the new architecture brings compared to their baseline BI capabilities, before deciding on whether to invest in new technology. We also help companies understand the costs of training their team members and of re-developing and supporting the old BI architecture until it can be retired. We then ultimately help our clients develop a business case for change (or not) based on these results.
Another way companies can accelerate delivery of the BI roadmap is by revamping their governance policies. Companies often underestimate the effect that restrictive governance policies have on the speed at which BI teams can iterate through metrics development. The same policies for changes to a data warehouse that are in place for a transactional system can result in wait times of days or weeks for simple changes to reports to be approved.
Fulfilling the Analytics Vision
Analytics is a never-ending organization for companies. However, developing a strong underpinning strategy and effective BI delivery organizational strategies will help companies understand what metrics and ad hoc capabilities are most important, and what the right investment level needs to be in their internal BI capabilities. To learn how PwC can help you realize your analytics vision, visit www.pwc.com/us/en/increasing-it-effectiveness/information-management.jhtml.