Month: March 2020

Just prior to joining Ness Digital Engineering, I did what most do when beginning a new position. I took some time to clean out my office, organizing my files and piles of reading material, remove some unnecessary clutter and get myself ready for the challenge ahead.

While doing so, I came across a copy of an article that I wrote back in 2015 while still a Partner/Principal at EY in the America’s Advisory group. The article was centered around what I referred to at the time as “the integrated data platform” and was largely focused on the concept of a unified (data) platform leveraging all data sources (structured, unstructured and semi-structured as well as external, internal and third-party) to solve specific business use cases. This was at the height of the in-memory solution explosion (led largely by SAP HANA) and wider adoption of the data lake at the enterprise level so the piece was a bit more focused on architecture than you will read in the paragraphs that follow.

As I read this article, I was reminded (and a bit amazed) how quickly the data and analytics space has evolved. From the days of multiple Hadoop distributors battling it out for market share with everyone in a mad scramble to create larger and larger data lakes. To the consolidation of that space into arguably a single solution provider which has worked arduously to extend their portfolio to remain a contender in this quickly changing data ecosystem. We have new, native cloud data repositories and analytic solutions that offer real-time insights, reduced cost and ease of use (in the hands of the business consumer) gaining enterprise-level adoption. There are new data acquisition and integration tools beginning to gain wider acceptance. And business intelligence is being replaced by “augmented intelligence” that offers deeper insights and guidance for the user via logical queries based on specific data sets.  I’m also reminded that with the sheer volume of data available, the ever expanding list of emerging technologies and solutions and the desire to turn ill-supported business decisions and haphazard guesswork into well thought out business decisions that can improve organizational outcomes will require an end to end approach ongoing data management and the dependencies and relationships across the data management landscape.

Given all the above, I thought it was time for a refresh of this piece with a deeper dive into an integrated data and analytics “Program” approach.  And, by a Program approach, I’m speaking to the unification of siloed and often competing projects/initiatives that don’t have the proper business visibility, a truly relevant and agreed to business use case, access to the proper data sets and lack real governance to succeed at an enterprise level. Data and Analytics Programs help address real business challenges and support the business from ideation to realization and offer a pathway towards extension of value leveraging data. Failure to create an integrated and optimized data landscape can leave the organization unable to derive actionable value from its investments. Organizations should, therefore, view data management as an ongoing enterprise program leveraging people, solutions and tools that are integrated and optimized to provide one cogent outcome, while ensuring that they can scale for enterprise-wide adoption.  Wearing my Advisory hat, I’ll focus on a point of view and some thoughts around building a firm data foundation that will enable the organization to leverage advanced analytics, Artificial Intelligence and other emerging technologies to extend the value of their most valuable, and often untapped asset (DATA).

First and foremost, it’s important to recognize the value that comes from organizations experimenting, attempting to stretch the boundaries of the possible, and desire to innovate wherever possible to gain a competitive advantage, reduce cost, or optimize processes. Unfortunately, many of these initiatives do not have the right visibility within the organization, lack enough funding, or don’t solve a broad enough business use case to be of value to the enterprise. This leads to many one and done projects/initiatives that are often seen as failures and can reduce an organizations appetite for further transformational initiatives. This doesn’t have to be the case if framed properly from the start.

A few key leading practices and (updated) suggestions borne from dozens of data and analytics strategy engagements with clients across nearly every industry sector who have sought guidance.

1. Executive sponsorship based on a clearly defined, socialized and agreed upon business use case. Many organizations are finding themselves awash with data yet unsure of how best to leverage and monetize it. Knowing where and how to start on a data journey, with clearly identified business use case(s) and a solid data strategy, can turn data into information, insights and profitable business outcomes. Understanding the value of both current and potentially available data can help prioritize and direct your investments in data and systems. Many organizations struggle to articulate the relationship between their IT investments and business value. Executive sponsorship is important. However, there is a difference between the top down and bottom up approach. Understanding how data drives business value can help you understand where you should be minimizing costs, and where you should be investing to realize the best business outcomes. According to Dorman Bazzell (Chief Data Officer for the State of North Dakota), “The State of North Dakota has Mars-Shot aspirations for data and analytics.” Bazzell shared that he and their forward-thinking team envisions a platform where any citizen – or anyone for that matter – can access an open data platform that can harness data from anywhere and provides tools to help shape analytics and recommend actions. he citizen data is owned by the citizen not the state, and provides a capability to self-create a personal, unique mobile application that delivers “North Dakota My Way.” Clearly North Dakota is thinking about their current and future uses of data and how to best leverage it and provide it as an added value to the citizens of their great state.
2. A longer terms vision of how it fits together from an end state architectural standpoint beyond the immediate project need. I’ve always preached – the way in which data is acquired, stored, integrated, prepared, analyzed and governed has changed over time and understanding how to effectively leverage this asset is key to success in the digital world. Further, when governance is layered over the framework of a data analytics platform, the result is a holistic understanding that ultimately improves the company’s ability to manage and measure ROI—a top concern for virtually every organization and business leader. The more companies invest in strategic analytics, the more they will need a robust data governance plan to ensure that proper results can be extracted from any given body of data.
3. An Advisory lead, technology agnostic view that builds trust and showcases depth of knowledge.  Not everything is a nail and an Advisory approach allows a strategic partner to solve complex business issues and provide guidance, support and implementation oversight as the organization looks to validate (or refine) their data strategy and provide value added solutions, leveraging trusted data having built a foundation that is scalable and sustainable. Advisory led translates to providing our clients with value added solutions leveraging information they can trust, and a foundation that is scalable and sustainable, providing optimal business outcomes.
4. A means to tie strategy to execution. As I articulated in a recent article (Data Management in the Digital Age: An Ongoing Journey), “If organizations are going to make the shift successfully to enterprise-wide, longer-term, more strategic projects, they need to consider solutions, tools and strategic partners that enable the broader data management vision. Implementation of both a service and governance model will provide a framework for industrialization of capabilities. These also will help the organization capitalize on the immense value that data can provide to the business. Innovative organizations will put proper organization and infrastructure in place to harness the true potential of monetizing the data assets of the organization.” It needs to be a true collaboration between IT and business with proper accountability.
5. Consideration of possible data privacy and regulatory compliance (GDPR/CCPA) implications.  An organization’s approach to data protection depends on the relative value it assigns to short-term profits versus customer goodwill and loyalty. A company focused on short-term profits may be tempted to extract as much personal data as customers will tolerate, sell the data, and take the minimum precautions to comply with data privacy regulations such as the EU’s General Data Protection Regulation (GDPR).  The downside is increased risk exposure. A patchwork of point solutions leaves the company vulnerable to breaches— potentially leading to fines, reputational damage, costly redevelopment efforts, and a battered share price. What’s more, customers who perceive a cavalier attitude about privacy feel justified in defecting to a competitor.  As Congress is placing more and more focus on privacy and continues their work towards a National Privacy Law, individual states are beginning to adopt new regulations that will need to be considered as an organization undertakes any data intensive program (e.g. Washington Privacy Act (SSB 6281) that is slated to be voted into law on March 12, 2020.)
6. Seeking Guidance for the journey.  It is important in this ever-changing data and analytics space, that organizations seek out support and align with a partner(s) that bring more than technical capabilities.  A true strategic partner can showcase “what the organization should be doing” and not simply executing on what the team has been asked to do. Technical challenges are part of the challenge. However, it is critical that one take into consideration the cultural and political challenges that often exist within an organization. Many organizations have a disconnect between the real time vision of leadership and their ability and appetite to adopt by those tasked with managing and leveraging organizational data. Further, there are some that may feel threatened by innovation and by those providing existing capabilities or services to the organization. Providing clients with true guidance and support and helping from ideation to realization and helping foster a culture where the organization expects value added outcomes from their data in a proactive approach.

The volume of data will continue to increase.  According to Gartner, today, fewer than 50% of documented corporate strategies mention data and analytics as fundamental components for delivering enterprise value. Yet, by 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.

The technologies to harness and exploit data will continue to evolve. Forrester states that “we are in the early stages of a technology revolution that will reframe the relationship between human and machine and between the customer and you.” They go on to state (regarding emerging technology solutions) are “not simply about technology; they are about the timing and nature of strategic choices you make.

What should remain a constant is how one approaches data management and analytics and the need for a more pragmatic approach to how we leverage data – looking at it as a true strategic asset and managing it as such.  By changing our mindset and taking a more holistic view on how we will manage and control the data that will continue to increase in its importance to our organization, we can then look for a seamless program on how data is stored, moved, checked for quality and safeguarded, ensuring that it provides us with the maximum value at the least cost and allows for increased flexibility in its use.