Transformation Analytics & Cloud Data Warehouses — Your Missing Link To Analytics Mastery?
Part 1: What are we not seeing when embarking on on analytics and digital transformations?
Digital technologies are becoming an increasing part of our personal and professional lives. The next decade presents an enormous opportunity for each of us. Yet spending well on enterprise analytics and AI to seize the digital advantage offered in the coming decade remains painfully uncomfortable for many executives and their respective teams.
Due to years of disappointing impact and returns, many are increasingly skeptical and rightfully cautious. Analytics business cases and underlying “digital transformations” are notorious for over-promising and under-delivering while consuming a more vital and significant piece of the budget pie:
- Enterprises, despite increasing investments and efforts, are making little to no headway to effective analytics and technology adoption.
- Many remain oblivious or unable to respond effectively to the magnitude of challenges that lay just ahead.
- A digital chasm opening between technology data and analytics masters and those that haven’t is set to determine the winners and losers of the coming decade.
They each have plans for digital visions C.2025. Does this urgent situation go deeper than just a Knowing-Doing Gap, and if yes, what can we do about it practically here and now to close the gap?
New Analytics and Transformation Business Case Inputs
These questions will be explored more deeply in the following posts. We will suggest a new way forward to remove the current obstacles for organizations and specifically their stakeholders, sponsors, talent and teams to become sustainable digital economy leaders.
The situation for many is not good. According to the latest c-suite survey published in HBR of significant enterprises by prolific (Competing On Analytics) advocate Thomas Davenport (Summary here):
- 92% of executives report the pace of their big data and analytics investment is accelerating
- 7.5% (yes) cite technology as the challenge
- 93% identify people and process obstacles as issues to adoption.
Meanwhile, growth in global data volumes within enterprises — fuel powering analytics and AI is soaring. A recent IDC survey reports:
- data volumes are growing at an average of 63% per month,
- with 12% of organizations reporting over 100% percent growth every month.
The following chart sets in context the current situation and the magnitude of opportunity and challenge in the decade ahead:
Fig. 1.0 illustrates the survey findings — the Winners Gap. Data and Technology growth is Exponential and People are Linear. Organizations’ capabilities are increasingly distinguished by digital enterprise talent technology and data leadership riding the curve. Is the dynamic drowning you or propelling you upwards?
Digital Natives like Amazon, Alibaba, Google, for example, are well on their way with the foundational skills to ride the exponential technology and data tsunami. They are defining the future. They are competing aggressively with one another for attracting deploying and exploiting talent, technology, and data. They are capturing rewards through massive growth and leadership recognition. All are growing exponentially from nowhere and dwarfing many traditional enterprises in the process.
Meanwhile, for traditional companies, evolving current approaches to achieving analytics leadership through talent team and technology are already proving ineffective.
Why are companies struggling with digital and analytics adoption?
You do have companies trying to make it easier to adopt analytics. But here again, you have myriad problems that ballast progression:
- They require business analysts to shuttle around data using inadequate self-service tools.
- When self-service quickly reaches limits, add long wait times for increasingly needed sophisticated curation and modeling services from experts and other data owners
- Facing prohibitively high fees when engaging both internal and external third parties to keep their solution relevant, business users seek new workarounds. Spaghetti IT increases complexity and compounds the technology data people and problems exponentially
Many solutions lock you into onerous long term support and maintenance agreements. Fees and inflexible change services lock down the technology, and they become irrelevant to business needs. Business users find new workarounds. It’s all very clunky, time-consuming, and expensive from a business consumer perspective, and debilitating from an enterprise performance perspective.
On the enterprise IT side, provisioning, implementation, rollout, upkeep, operations fees can quickly rise against a backdrop of diminishing returns. Either through unplanned costs due to new commercial pay as you go scaled adoption, or sunk charges against a diminishing user base in a traditional pay-for-provision commercial model: either scenario destroying the business case and long-forgotten investment theses.
Lastly, the other major problem is people don’t like change — especially around transparency, ways of working, and a new focus. There’s a regret that comes from implementing new insights and automation technology only to see behaviors revert to normal.
So a winner in the enterprise analytics and digital foundation space needs to solve three problems:
- It has to make enterprise scaled technology adoption underpinning analytics capability fast easy and cheap to integrate
- It has to make enterprise consumer technology adoption engaging fast easy and affordable to consume
- It has to have a fast, low-cost sustainable way to allow data and insight consumers and providers to adapt as their needs evolve, so they’re collectively incentivized to invest time to keep the solution relevant
Quick Test: Where Are You Today On The Analytics Maturity Curve?
Ambition Capability — Analytics 4.0 | The Cognitive Era
Consider for a moment at which stage you are as an organization on this chart for each of your analytics investments? For how long and for how much investment and result?
For all the talk of AI, Cognitive analytics and becoming data driven, most traditional organizations remain stuck between Stages 1 and 2.
Every enterprise analytics sponsor with a Stage 4.0 Cognitive Analytics ambition should ask:
- How many of our struggling current analytics and transformation initiatives are stuck between stages 1 and 2 and on their current trajectory set against the exponential benchmark are already doomed to failure?
- At which point will it be impossible for us to make up lost ground between ourselves and our digital-native competitors?
- What can we do now to ensure we can both span and leverage the ever-expanding People Data Technology chasm and not fall victim to it?
We will address these pressing questions and more, in Part 2 using this industry benchmark maturity model from Thomas H Davenport. We will illustrate the maturity framework with the latest advances in Cloud Technology and Analytical Applications and ways to embrace the exponential shift, to secure your present and future business outcomes.
First things First — Your Talent and Team Hold The Keys
There are no silver bullets, but as noted above, the full range of advantages and disadvantages are unleashed using the people factor fused with technology and data. The state of the present technology is such that people augmented with technology and empowered the right way lead to the most significant advantage.
As every enterprise must now become an intelligent insights-data-technology driven enterprise as a core competence, we would do well to consider the new role of the intrapreneur and start-up mindsets and what VC’s know what to look for to capture success:
Great ideas without execution go nowhere
“Rather than focusing on your idea, there’s a good chance an investor will want to know more about you and your team. Even if you have an amazing idea, it won’t take off without the proper execution.
On the other hand, a mediocre idea has the potential to succeed with a competent management team determined to make it work. A venture capitalist or angel investor wants to know that you are a leader capable of follow-through.
If you are the type of person who gives up when things get tough, the startup is likely to fail, and an investor will lose their investment. Rather than risk that, many venture capitalists and angel investors will look at your track record of persistence and grit, rather than get hung up on your idea”.
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