the needs of our customers. Where we are doing things that are extremely positively received, we do more of it! One way we can directly react is through engagement. Our service provider gives us an added drill-down capability that allows us to hone in on specific observations that appear as outliers, whether positive or negative. We then have the opportunity to respond through an active feedback loop. We can reinforce positive sentiment and respond to or even turn around negative sentiment through this channel. The other key component here is having a highly trained internal team to take swift action when the time comes. Our Google review stats are proof that we understand the importance of keeping consistent excitement around the Lash Affair brand. Data analytics capabilities have given us a giant advantage in getting this done.
Paul Paris, CEO at Lash Affair.
Conclusion
In this chapter, we have mentioned a number of important emerging analytics tools. We discussed the vast explosion of data that we have witnessed, partially due to the “internet of things” and all of the various connected objects we interact with each day. Cloud storage and cloud computing have emerged to efficiently store, access, and interact with this flooding sea of Big Data. We introduced artificial intelligence as a broad subject, encompassing many fields of study, that is an incubator for data analytics technologies and disciplines. From it have emerged OCR and ICR, as well as natural language processing and language translation disciplines. We highlighted blockchain and distributed ledger technology as an important means of crowd-sourcing consensus to agree to the true state of ownership for assets, or to agree to the golden source of truth that many can subscribe to and consume, to eliminate the need for reconciliation and to reduce investigative efforts that are undertaken to resolve differences between disparate sources of data. We discussed the role of robotics in performing structured, repetitive processes quickly and more efficiently, in the reduction of manual-intensive tasks that distract humans from focus, higher-order analysis, and actualization.
Your mind was already blown, your teacup was nearly empty except for a bit of floating sediment, and then … self-service data analytics was introduced to lend structure to spreadsheet-based processing steps, to build efficiency, and to bridge the gap until strategic systems can deliver needed interoperability or enhancements! Critically, we submitted that the move toward self-service analytics represents a de facto decentralization of change, from being in systems, to being around or outside of systems and far more focused on automating individual spreadsheet-based processes. We pointed to the rise of self-service data analytics as putting digital instruments of change directly into the hands of process owners, rather than only in the hands of technologists. Finally, we have stressed the inroads made on dashboarding and visualization to communicate processing results and key business performance metrics in a way that gives color and context to the numbers being presented.
Having introduced these key enabling analytics technologies, we will ask the readers to consider how they have an obvious impact on their daily lives. Clearly, you will have interacted with many of these with some frequency. We have provided only a high-level introduction to these topics here, when volumes can and have been written on each of these, individually. The idea was to take some of the fear out of these buzzwords and to show how they are beginning to change the way we live, what we know about consumers and customers, how we transact business, and how we perform our daily jobs.
In this chapter, we treated self-service data analytics as if it is just one of many in a new breed of intelligent analytical tools – and that is true. But they are a worthy focal point for the majority of this book, as they represent a seismic shift in how change is delivered across large organizations. The impact of the shift can be seen both in how we work individually and the pace of process change and digital advancement across organizations. These tools are steering technology budget allocations toward the licensing of off-the-shelf tools, and they are influencing the size and shape of the technology arm of organizations. Clearly, they are impacting how we staff our processing functions and the new skillsets that are valued. This set of tools is driving an observable shift in large organizations across a number of dimensions.
This evolution comes with significant benefits, but also with significant risks. As the pace of change accelerates, the need for strong overarching governance is most pronounced. In Chapters 3, 4, and 5, we will describe how the introduction of self-service data analytics tools will open the floodgate for rapid adoption. We will demonstrate how the shift to decentralized change renders much of the governance apparatus that may be mature in legacy system-centered organizations as incomplete, if not superfluous and irrelevant. We will systematically highlight the gap in available governance frameworks, when it comes to structuring controls around self-service analytics builds and tooling. Most critically, we will draw essential elements from a host of fragmented and overlapping governance models and extend them to forge a new, dynamic, and comprehensive governance framework to fit the new landscape of self-service change.
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