Going Deep and Wide using an Enterprise Approach for Analytics
“End-to-end (also known as E2E) in supply chain management refers to the end-to-end process in the supply chain. It involves the process in its entirety, starting at the procurement of materials from suppliers and ending when the product reaches the customer."
Getting value from analytics is a headline that is constantly being offered daily in multiple publications. A lot of what is said about analytics is really just a spin on ways to sell “re-wrapped” products and services. My hope is that the information that is being offered in this blog provides meaningful suggestions that allow your company to gain significant competitive advantage from analytics.
Companies are aggressively trying to figure out what analytics means and how they can tackle the multiple obstacles they anticipate in order to gain significant value. I want to demystify the whole notion of harnessing Big Data analytics from being a seemingly impossible, daunting task to an opportunity to create an invaluable asset that drives significant financial performance improvements. In the last blog, we talked about building organizational consensus and in this blog, we will look at the importance of building on that consensus to support an enterprise wide approach to maximize the value of Big Data.
The book Deep and Wide by Andy Stanley is on a completely different subject, but the title of the book provides synergy to taking an enterprise wide approach with Big Data. Let’s explore this further.
Value across the organization
To tackle the point of gaining cross-functional buy-in to your analytics approach, you need to give everyone at the party something they want. But how can you accomplish this to satisfy the specific analytical needs for Supply Chain, Sales, Marketing, Operations and Finance? Doesn’t this only increase the time and complexity associated with the effort and push it one step closer to inevitable cancelation?
The answer is no. Effective solutions do not approach Big Data analytics from a “functional silo” point of view but from an “end to end supply chain” perspective in order that the transactional data can be repurposed to serve the multiple needs of the organization. Taking a holistic approach that includes suppliers, manufacturing, storage, transportation, inventory and product returns provides an end to end level of scope that can then be used to serve multiple functional needs. This is the Wide piece of the puzzle having one source of trusted information and is a key criteria to building effective analytical solutions using Big Data.
But what about the Deep side of the solution. To be meaningful, Big Data analytics have to provide for meaningful insights that drive better organizational decisions. This requires the ability to get to very specific performance information, information that provides insights and is actionable. You might be thinking; what, detailed information about end to end operational performance accessible to multiple organizational users? You bet! The cloud now provides the processing capability to take the first step to harness Big Data analytics that drives value by increasing financial performance and competitive advantage.
I am often in meetings with a group that was not the original sponsor of the company’s Big Data analytics initiative. The conversation will go something like this:
“Yes, we think that’s the case, nothing new there. But wait, look at those details! Can that be right? How did you determine that? Can you drill into that further?
I wish we had known that specific information on that (customer, product, channel – you pick the area) yesterday.”
Avoiding approaches that perpetuate silos of Big Data in analytical solutions is a huge step in gaining high impact results. Making very specific operation details available in ways that are meaningful to each organization eliminates conflicting analysis and confusion on operating performance. Going Deep and Wide using an Enterprise Approach is key.
In fact, this goes back to getting everyone to buy in. Give everyone something to make their job easier, something to make them smarter and something that makes them more efficient and you will have turned data into an asset instead of it being a liability...
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