Together, our Data and Analytics service areas combine to form our core focus on Integrated Analytics. This is the central premise that the company was founded on, so that we could help clients to eliminate the disconnect between all of the relevant components that are required to work in concert to turn information into a valuable business asset. As reflected in our logo, this Data + Analytics is represented by the following four quadrants, or practice areas:
TCC has the knowledge and experience to identify the key building blocks that drive success (or failure) in analytics initiatives. As a result, we understand that the fundamental success factor is to ensure that the data foundation is well designed and implemented to support desired front-end user capabilities. This discipline includes three distinct components:
The value of data initiatives is frequently under-appreciated because their impact is not well understood by business sponsors, and they do not deliver exciting new interfaces to business users. It is however well-recognized among analytics experts that approximately 60–70% of the work required to complete end-to-end analytics projects is spent on the data layer.
Does this mean that all analytics projects require this amount of data work? No, because all organizations have some form of data architecture in place, and many are sufficiently developed to require only best practice or optimization adjustments which can be identified through a data architecture review.
Nevertheless, putting the appropriate focus on a proper data foundation is critical as it will provide a solid, flexible and robust platform on which to build or combine front-end capabilities moving forward. Subsequent analytics initiatives will therefore be quicker, easier, and more dependable. Moreover, a solid foundation will actually open up a much wider range of business options that will enable the organization to save time and money in delivering the right capabilities to meet the needs of their users.
To help link the data layer with the desired business user capabilities in a cohesive and effective approach, TCC typically starts with our Data Governance Assessment process. Once this has been done, we can help the organization map the user requirements to the data architecture design and toolset options, and deliver new capabilities quickly and cost-effectively.
As with most software industries, Business Intelligence (BI) has undergone several innovation-consolidation cycles over the years. The most recent consolidation cycle a few years ago saw a number of very large software vendors attempt to establish an end-to-end BI portfolio, with the following solutions comprising a complete BI suite:
Although the current innovation cycle has produced a variety of new standalone BI offerings, the reality is that the rapid convergence of capabilities across vendors has resulted in commoditized feature-sets. As a result, organizations are understandably confused by the almost indistinct messages coming from different BI vendors, which make it difficult to determine the best approach to support an Enterprise BI Strategy or deliver successful BI projects.
In the meantime, “Big Data” volumes are creating a huge volume of information for organizations to manage, and bringing new emphasis to the oft-ignored reality that BI capabilities for business users are only as good as their data infrastructure, and that the success of a BI initiative relies as much on the quality of the back-end as on the selection of the front-end toolset.
This current marketplace presents both a great opportunity and a difficult challenge: it requires specialized skill or insight to assess BI user options and identify best-of-breed solutions for each user group; and successful BI projects demand expert knowledge of the interdependence of the data and BI layers.
Despite the availability of robust enterprise PM tools, recent studies have confirmed that 85% of CFOs still rely heavily on Excel spreadsheets for:
In fact, over 50% of companies use Excel as their principal interface for budgets and forecasting, and almost exclusively for planning.
Although it is a valuable personal productivity tool, Excel does not provide the key features necessary to ensure stable, accurate, and reliable outcomes for financial close: access control, auditability, and workflow. Most procedures involve manual, multi-step task sequences which usually involve copy-pasting of data. Large workbooks and linked cells can cause crashes or data integrity issues. Not to mention that email is usually the preferred method for distribution, review, and approval. This results in spreadsheet proliferation which makes it very hard to manage versions, ownership, and workflow status. Managers have no centralized view of the end-to-end process and can’t ensure accuracy or completion. Most organizations have a hard time documenting these multi-step, manual processes, let alone managing them.
Investment in the right Performance Management strategy is critical to reducing cycle times for financial close as well as budgeting/planning. We take a disciplined approach to mapping-out these procedures, focusing on automation, systems integration, and streamlining user-task sequences. We deliver projects that reduce close times, improved accuracy, and provide managers with the ability to monitor and administer workflows with complete visibility into progress and results.
Over time, the labels used to describe the practice of deriving business insights from data have changed quickly from “reporting” to “decision support,” and on to “knowledge management,” before finally settling on “business intelligence.” As the BI category expanded over time to include a broad suite of applications, including cross-over into data management and performance management, it has often become easier to capture all of the pieces under the single heading of “Analytics.”
Unfortunately, this also caused confusion by blurring the traditional distinction between BI and “Advanced Analytics” (AA). Simply put, AA is used to describe predictive modeling, or the practice of using complex algorithms to identify patterns that predict future behaviour. Neural networks, K-squared analysis, or decision trees are examples of well-known AA models. Although several BI vendors are beginning to develop their own versions of these applications, this market has been dominated by a very limited number of applications.
These solutions are most commonly used in financial services for fraud detection, or in retail to predict consumer behaviour. There is however a gradual evolution to apply these capabilities for other purposes, as the next logical step once a traditional BI framework has been established. Despite becoming more user friendly, the use of these applications is still confined to a small, dedicated group of analysts within any organization.
More recently, this has expanded to include the Internet of Things, Machine Learning, and Artificial Intelligence, and has given rise to a whole new category of solutions.