Navigating the BI and Analytics product landscape
Paul Norris is head of BI and Analytics capability at Version 1. The firm’s BI & Analytics capability enables businesses to be data driven in their decision making, from ad-hoc analysis through to strategic direction setting and day-to-day operations.
How do you navigate the BI Product Landscape?
Every organisation can benefit from a well implemented investment in BI & Analytics products. Benefits range from efficiency improvements such as reduced operating costs to new strategic insights that can transform an organisation’s management and embed sustainable long term competitive advantage.
The BI & Analytics product landscape is vast with each vendor claiming to have the right product to meet your requirements. Annual industry publications attempt to aid the buyer by categorising and shortlisting the products into broad functional areas. While these publications act as a useful starting point when first exploring the product landscape, they do not highlight what is the key fact that buyers need to understand and that is that there is no single BI & Analytics product on the market today capable of meeting all of an organisation’s BI & Analytics
long-term requirements.
There are products that excel in areas such as data discovery and visualisation, but are weak at data integration and preparation. Likewise, there are integration products that have no data discovery functionality but are leaders in the integration space. The best solution to meet an organisations BI & Analytics requirements is often a well-designed selection of multiple products, each a leader in their own functional area and capable of returning value many times the initial cost through reduced implementation times, reduced support and maintenance costs and use of agile development methodologies.
When assessing the BI & Analytics product landscape where should you start?
My advice is to first focus on the business value that can be unlocked by such an investment and not focus too early on the products themselves. A well-defined value proposition will make it obvious which products should be considered by defining the functionality required and the budget available. If you do not want to make a large investment initially then you should choose tactically and select a product that will both deliver positive short-term results and demonstrate the value BI & Analytics product can deliver to senior stakeholders. While the tactical products may not scale to meet your needs in the longer term they will help to build the case for a larger strategic investment.
When the value of a larger investment is clear it is useful to divide the product landscape into three broad categories and consider having to make separate purchases in each. These are; data integration, data storage and data presentation products. The sustainable long-term IT investment is often best made in the data integration and storage layers while the business investment is better made in the presentation layer.
Increasingly data savvy business users and departments are purchasing products in the presentation layer category independently of the IT group and gaining significant return on investment but failing to embed the gains back into in the organisation’s data landscape. This new buying pattern is here to stay and the IT department’s role is evolving into one where they are facilitating cost effective data integration of the organisation’s datasets into secure, scalable data storage layers where business users can consume the data using whichever presentation product they prefer to use.
What is the role of IT and the Business?
This approach of IT focussing on the long-term date integration and storage and business on usage and presentation ensures that the organisation’s data integration and storage investment persists beyond the latest trends in presentation layer tools and that business users have high quality, up-to-date datasets available to them at all times while being free to use their preferred product. This approach is especially important when an organisation moves into the use of advanced analytics and develops a data science capability. At this point, the lines between off-the-shelf products blur with custom analytical solutions written in programming languages, such as R and Python, which consume datasets directly off the data storage layer and the presentation layer is no longer obviously defined.
About the Author:
Paul Norris is head of BI and Analytics capability at Version 1. Version 1 BI & Analytics capability enables businesses to be data driven in their decision making, from ad-hoc analysis through to strategic direction setting and day-to-day operations. Their data driven customers are proven leaders in their industries in that they treat their data as an asset and use it to gain advantage over their competitors. Version 1 capability enablement approach guarantees that every customer receives the most comprehensive set of data management services available today. They are constantly innovating and adopting the best methods available to ensure that they deliver maximum value to their customers.
For more information contact Paul at
paul.norris@version1.com
