Strategically Navigating The Analytics Landscape Puzzle
Over the past year, there have been numerous “big” mergers and acquisitions which have taken place in the analytics and BI space. May 2019 saw Sisense and Periscope merge their companies to combine their efforts and offerings. This was followed up by the massive acquisition of Tableau by Salesforce in August which looks to bolster both the visualization capabilities within Salesforce along with expanding their portfolio. Even with those deals, the acquisition of Looker by Google in June 2019 and the partnership in June 2020 between SAS Viya and Microsoft Azure, stand out even more because these latest moves occurred in the ever-growing cloud analytic space.
The days of “best-of-breed” as it pertains to BI and analytics software is quickly fading away as “end-to-end” analytic solutions via the cloud are becoming the norm. Whether you are talking about AWS, Google Cloud, SAP Cloud, Microsoft Azure, and so forth, the race by these vendors to improve their offerings and capabilities is happening with full force. However, what do all these moves mean to the customer who is trying to increase their decision making capabilities and needs a start, the organization who is moving for the sake of moving, or the customer who has some semblance of an analytics strategy but has to deal with the shifting landscape? Specifically, with cloud analytics, there is still much to understand about how these solutions deliver in terms of ROI and value. Studies have shown that leveraging the cloud for analytics do in fact decrease the Total Cost of Ownership (TCO) as well as reducing “Time-to-Insights.” However, the decision on which platform or sets of tools to use, needs to be firmly rooted in the overall analytics strategy of the organization.
It would be very easy to contract with a consultant or start procuring the various pieces in a selected cloud solution and just start building away. However, is the “something” your building aligned with the organization’s analytics strategy and focus? Will it provide the value you seek for the money and time you will be investing and spending? Can the implemented solution allow you to compete better in your industry or market? If one does not put these questions through the rigor they demand, I contend that what will be delivered in the end is an over-engineered or potentially inadequate solution that does not really solve anything. This is where I feel that performing a tried and true SWOT analysis of an organization’s analytics ecosystem can go a long way to getting your “data driven decision making” house in order.
Example SWOT Analysis for Assessing the Analytics Ecosystem
While I understand that a SWOT analysis is traditionally used to assess the overall strategic industry position of an organization or product viability, I actually see a relevant application of it to not only understand where you are analytically as an organization and where you want to go, but also to ensure that the acquisition of the relevant tools and technologies are in good alignment with this position. Yes, if the organization is large and diverse with respects to data, contracting out this work may be appropriate. However, there is nothing stopping one from getting a baseline idea of where they stand by doing this themselves.
In looking at it, the first two parts, “Strengths” and “Weaknesses” are straightforward – what do you do well and not within your analytics “ecosystem.” In the example above, I chose to focus on the data, delivery, real-time analytics, advanced analytics, and people simply because these could be considered key pillars to efficient organizational use of data and information. Understanding where you are for these pillars is key to the rest of the exercise.
I see the “Opportunity” category as the place where one should critically think about where the organization is, and what platform, tool, or processes can take the organization to the next level on its analytic journey. This category should not solely focus on the identified weaknesses. Rather, one should look for key strategic areas that could be bolstered by these investments into analytics. For example, maybe you believe that you can increase customer sales using AI for pricing optimization and marketing? Maybe you simply want to better organize your data and start small just to simply learn some basic insights to identified KPI’s? Whatever the case, there is always some aspect of the business that could be improved or enhanced with technology and this is the area to identify it.
Lastly, but certainly not least, is finding the “Threats.” While there may be a need and a willingness to invest in the analytics initiative, the threats to it must be identified and put under risk mitigation. In my mind, one of the biggest threats, in addition to budget, is not having the staff and/or skills to put the plans in place. What’s the point of spending time and money to implement a strategy and procure a solution when your staff simply cannot leverage the tools effectively? Making sure you are well aware of all the financial, technological, and skill issues that exist is critical to the overall strategy and need consideration for the technology foundation design.
The analytics technology landscape will undoubtedly keep morphing both in tools and platforms. To keep up with the change, organizations must have at least some sense of their analytics strategy, if not formalized, in place and implement solutions in alignment with that strategy. Even though standalone tools still have a place, cloud-based data and analytics platforms bring many new and exciting features. However, their “newness” also brings with them new challenges that are far different than what was present in the in the past.
