There was a time when analytics meant implementing a huge Business Intelligence (BI) system, spending a lot of money and landing up with a project that would run into several years. Naturally, a wide chasm emerged between the “haves” and “have-nots”. You either had a system where all your data was cleanly (or not so cleanly!) pulled from various source systems into a common data warehouse from where charts and information were used for decisions; or, you had “analysis” (rather than “analytics”) happening in different parts of the enterprise with each unit pulling its own data from its own source system and making it work for its particular needs.
Interestingly, the reason for not investing in analytics was not always the lack of funds but rather the lack of a strong business case for such a huge expenditure since the ROI was rarely clear. Further, such systems were always owned by IT rather than the business, which made the business case even more obscure.
As the years progressed, while the overall ROI remained unclear, BI systems started to provide uniform global views of data, and standard information began to be accessible to diverse parts of the organization. The technology kept getting better and the gap between the haves and have-nots continued to widen with every upgrade.
Then something very surprising happened – there came a tipping point. The curve hit a peak and started a downward spiral. Analytics started becoming more democratized than ever before. The nature of data and technology changed; but more significantly, as newer analytical needs emerged, the very definition of analytics changed and traditional systems found themselves struggling to keep pace with business user requirements. Not only did this development create new opportunities, but it also shifted the focus from technology back to talent.
Let’s look at these changes in the analytics landscape more closely.
- Changing data: The term “big data” comes from the exploding volume, variety, and velocity of data. Traditional systems struggled with this, giving way to new entrants such as Hadoop which have a drastically different approach to the way they store and process data. This has opened up more flexible options for those who did not go the BI route.
- Changing analytical needs: Both external and internal factors have pushed analytics from being an “information provider” to being a prescriptive decision enabler and a predictive indicator, helping companies stay ahead of the curve. What the businesses need is not trends but simulations and complex data crunching to give them options in a way that meets specific objectives and enables them to act very quickly based on real-time data.
- Changing technology: In order to get to the prescriptive or predictive element, or to obtain even basic visibility, it is no longer essential to go whole hog on having a data warehouse. Flexible options from newer technology players and targeted modules from large, established players now enable easy data upload, consolidation, and consumption – be it for visibility, post-mortem analysis, or more advanced analytics.
- Cloud Adoption: The move from on-premise systems to the cloud has greatly increased affordability. Without compromising on data security, companies can scale up or down based on their needs. The now commonly accepted “pay-as-you-consume” model helps experimentation and projects to take on their own life without becoming cumbersome.
- Platform-based analytics: There are now analytics platforms targeted at very specific use cases. These are industry focused (e.g. consumer products or financial services) or function focused (e.g. sales or supply chain) and are more flexible. Being mostly cloud-hosted, their implementation can be as small or large as the user requires.
- Integration of analytics with ERPs and operational platforms: This is the real game changer for analytics. ERPs are themselves undergoing a radical change. Increasingly, all new operational platforms are being launched with analytics modules built into the configuration. While most of these now focus on the visibility and descriptive phase, their roadmap is sure to include prescriptive and predictive elements. Some examples of analytics built into platforms include:
- SAP S/4HANA for finance operations comes with prepackaged analytics and connectors to plug into many other analytical or visualization tools.
- Salesforce has many features built into its CRM that can provide standardized or customized analytics to the user.
- The increasingly popular Workday tool for HR operations includes most metrics.
- Open Source Software: For companies where cost is the primary factor, the open source wave has unleashed a variety of options. All you need is a bunch of very talented analysts and they can do wonders with free software such as the very popular “R”.
While the above changes have significantly narrowed the aforementioned gap, it doesn’t mean that the quality of analytical information available to all has reached the same level. The final and perhaps most critical differentiator is talent. The key to successful analytics is defining the problem statement or objective, knowing which analytical technique or technology to apply to which objective, applying it effectively, interpreting the results of the analysis, and converting these recommendations into actions that make an impact.
Business users now have an abundance of choice and analytics is more accessible to all than ever before, requiring far less time, effort, or money. If you have not invested much in analytics in the past, now is the time to grab the opportunity and level the playing field.
This article was written by Divya Kumar from Capgemini: BPO Thought Process and was legally licensed through the NewsCred publisher network. Please direct all licensing questions to firstname.lastname@example.org.