Tag Archives: data

Turning Big Data From A Liability To An Asset – Point 2 Taking An Enterprise Wide Approach

Richard Sharpe Analytics & Big Data
 

Turning Big Data From A Liability To An Asset – Point 2 Taking An Enterprise Wide Approach

logistics

Getting value from Big Data is a headline that is constantly being offered daily in multiple publications. A lot of what is said about Big Data 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 Big Data.

Companies are aggressively trying to figure out what Big Data means and how they can tackle the multiple obstacles they anticipate in order to gain significant value. This posting is the second in a series focused on the problem of addressing data liability. The goal of this series is to demystify the whole notion of harnessing Big Data from being a seemingly impossible, daunting task to an opportunity to create an invaluable asset that drives significant financial performance improvements. In the last issue, we talked about building organizational consensus. In this issue, we will look at the importance of building on that consensus to support an enterprise wide approach to maximize the value of Big Data.

You might find the following article of interest. It also addresses the need for an enterprise-wide approach to Big Data: click here

I am currently reading the book Deep and Wide by Andy Stanley. Although the topic is on a completely different subject, the title of the book provides synergy to taking an enterprise wide approach with Big Data. Let’s explore this further.

To tackle the point of gaining cross-functional buy-in to your Big Data approach, you need to give everyone at the party something they want. But how can you accomplish this to satisfy the specific Big Data 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 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 Integrated Business Planning (IBP)solutions using Big Data.

But what about the Deep side of the solution. To be meaningful, Big Data solutions 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 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 initiative. We will be reviewing IBP information focused on a specific operating strategy. 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 use silos of Big Data 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...

We would appreciate hearing your thoughts and comments. All the best, Richard

Richard Sharpe

Richard Sharpe is CEO of Competitive Insights, LLC (CI), a founding officer of the American Logistics Aid Network(ALAN) and designated by DC Velocityas a Rainmaker in the industry. For the last 25 years, Richard has been passionate about driving business value through the adoption of process and technology innovations. His current focus is to support CI’s mission to enable companies to gain maximum value through specific, precise and actionable insights across the organization for smarter growth. CI delivers Enterprise Profit Insights (EPI) solutions that enable cross-functional users to increase and protect profitability. Prior to his current role, Richard was President of CAPS Logistics, the forerunner of supply chain optimization. Richard is a frequent speaker at national conferences and leading academic institutions. His current focus is to challenge executives to improve their company’s competitive position by turning enterprise wide data from a liability to an asset through the use of applied business analytics.

Turning Big Data From A Liability To An Asset – Point 1 Organizational Consensus

Richard Sharpe Analytics & Big Data

Turning Big Data From A Liability To An Asset - Point 1 Organizational Consensus

cross-functional consensus

 

In my last blog on Big Data we discussed how Big Data can support 4 distinct forms of decision making: Descriptive, Prescriptive, Predictive and Cognitive Analytics. However, in order for any analytical solutions to be meaningful, the issue of data liability needs to be addressed.

Starting in this posting and in the next three (3) releases for this blog, I am going to offer a series of points, which help solve this data liability issue. The goal is to demystify the whole notion of harnessing Big Data from being a seemingly impossible, daunting task to one of an opportunity to create an invaluable asset that drives significant financial performance improvements. Naturally, there are a whole host of actions required to fully harness Big Data but in this series we will focus on just four: building organizational consensus, taking an enterprise wide approach, placing priority on data governance and finally creating repeatable and measurable impact.

While I was the President of CAPS Logistics for over 8 years, the company serviced over 16% of the Fortune 500 in various forms of supply chain decision support applications. These efforts spanned the globe from supporting the expansion efforts of a worldwide soft drink company, to the redesign of supply chain networks for multiple CPG and specialty manufacturing companies in addition to providing different transportation solutions to one of the largest companies in the waste management industry. The technology was proven, the people were bright and the focus was to provide the best solutions possible. Unfortunately, in certain cases, the implementation of these carefully engineered solutions was either slowed down or even put on hold. Why? Other departments would offer objections often citing concerns about the “quality” of data used for the analysis. Typically, the nail in the coffin would be that if they would offer that their data concerns were correct, the integrity of the solution could cause the company to miss its revenue targets. Sound familiar? The end result was that the company did not attain the competitive advantage that was possible.

Let’s take this lesson to the world we live in today, a world of exponentially growing data associated with the operation of your enterprise. The problem sited above had two issues. The “quality” concern which will be addressed in a future posting on data governance. The second issue was not having cross-functional consensus. In the projects mentioned above, efforts were made to involve other departments at every stage of the project but gaining supply chain efficiencies were not their highest priority. Therefore, these other functional groups had no real buy-in in the work effort.

If you believe that Big Data has the opportunity to drive significantly recurring financial improvements, then the table stakes are even higher with regard to early organizational buy-in. Buy-in with regard to getting everyone on the same page on how to repurpose and use Big Data to empower multiple forms of new analytical and strategy development capabilities. I am guessing that many of you are saying “yea, right for our company; this would be comparable to building Noah’s Ark.” You may be right but it is a challenge that deserves careful consideration as offered by the following quote by Benjamin Franklin:

“By failing to prepare, you are preparing to fail.”

Success will require having the right level of Executive sponsorship and taking advantage of the power of cloud technologies and proven methodologies. In addition, other key points need to be integrated into your approach including obtaining cross functional consensus on any data issues. If this is not a mandate, you run the serious risk that organizational questions will surface which can stop the entire effort.

So why go to the effort? Isn’t the political risk too significant and the task too big to undertake now especially given all of the mission critical projects that are already established for the company? That might be the right answer but let’s look at it another way. Is it possible that your Board and certain members of your Executive Team are wondering if the company will be left behind when your competitors harness the value of Big Data? Value that is measured by increasing financial performance and competitive advantage. Only you know the answer to that question, but if the decision is made to take the first move advantage, be sure the process you use requires cross functional organizational consensus as a cornerstone of the solution.

Check out this video, which can help get your cross-functional team invested in Big Data: https://youtu.be/DCffaA-Cra4

We would appreciate hearing your thoughts and comments. All the best, Richard

Richard Sharpe

Richard Sharpe is CEO of Competitive Insights, LLC (CI), a founding officer of the American Logistics Aid Network(ALAN) and designated by DC Velocityas a Rainmaker in the industry. For the last 25 years, Richard has been passionate about driving business value through the adoption of process and technology innovations. His current focus is to support CI’s mission to enable companies to gain maximum value through specific, precise and actionable insights across the organization for smarter growth. CI delivers Enterprise Profit Insights (EPI) solutions that enable cross-functional users to increase and protect profitability. Prior to his current role, Richard was President of CAPS Logistics, the forerunner of supply chain optimization. Richard is a frequent speaker at national conferences and leading academic institutions. His current focus is to challenge executives to improve their company’s competitive position by turning enterprise wide data from a liability to an asset through the use of applied business analytics.

How Can Big Data Drive Better Decisions?

Richard Sharpe Analytics & Big Data
 

How Can Big Data Drive Better Decisions?

analytics

I just recently returned from the CSCMP Global Conference. While at the event, I had a number of Big Data conversations were people were asking questions like;

“Is it possible today to use Big Data to support meaningful business decisions?”

I walked away from the conference thinking that my next blog posting needed to further address this question.

You probably have heard of Big Data being used with the four following terms; Descriptive, Prescriptive, Predictive and Cognitive Analytics. The terms can be a little confusing. Each of these forms of analytics have been around for a long time but with the incorporation of Big Data, they each offer tremendous new opportunities to drive significant value to making better informed business decisions. Let’s explore each in the context of Big Data and improved decision support.

Descriptive Analytics – as the name implies, the focus is on describing historical performance. Nothing new about this you might say. But with Big Data, the level of performance information can be very specific by product, customer, channel, supplier and other key operational areas of focus. What is an example of this? At the Gartner Supply Chain Conference in Phoenix this spring, it was offered that companies will need to evolve from using a “standard cost to serve model” to a “total cost to serve model”. To accomplish this, a company must take a far more comprehensive approach that includes all direct and indirect costs associated with fulfilling each customer order. This requires validated data to be organized and applied in a repeatable process in order for the organization to have confidence in the information and to make it actionable. There are many more examples of how Descriptive Analytics, using Big Data, has the opportunity to identify immediate revenue and cost opportunities that are not visible with aggregated or disconnected information.

Prescriptive Analytics – this type of analysis focuses on defining different ways that you may operate your supply chain given certain assumptions, business rules and anticipated customer demands. A very common form of Prescriptive Analytics is in the use of network or inventory optimization applications. However, as we all know, any form of analysis is only as good as the quality of the data that is being used. Big Data’s contribution to putting Prescriptive Analytics on steroids is that companies no longer have to settle on what data they can assemble to support the work. By mastering Big Data, organizations can now have complete and accurate data to be creative in intelligently organizing information to meet the exact needs of the analysis. This, in turn, provides a more robust and accurate prescriptive business solution.

Predictive Analytics – this one is easy. Everyone has some form of forecast for the future. For most companies, forecasted customer demands play a primary role in their S&OP meetings. So how does Big Data support Predictive capabilities? The answer can be found in three ways. First, as noted above, getting everyone on the same page with regard to exactly what did happen provides for a “fact” versus “opinion based conversation. This has an obvious and very positive impact on discussing the future. Second, Big Data can also provide insights regarding more accurate trending and patterns because it is more specific and accurate than higher level or “silos” of operational data. Third, utilizing this information in predictive analytical tools like simulation applications provides a more accurate way to analyze the impact of specific decisions on future operating performance.

Cognitive Analytics – this fourth area of decision support also offers amazing new opportunities to positively impact financial performance. Having one source of data that has been created from all relevant structured and unstructured data sources provides a detailed picture consisting of operational patterns , trends and informational facts that can be linked together to provide valuable insights. But with advances in Cognitive Analytic capabilities, machine driven learnings that get smarter over time, this form of analytics has the potential to further drive innovation and competitive advantage. Blending the intelligence of the people who know the operation with the power of increased machine driven operational insights will be a big part of how companies will take additional advantage of Big Data. Although early in its development, Cognitive Analytics will be quickly adopted by those companies who have mastered the use of Big Data in their Descriptive, Prescriptive and Predictive analysis activities.

So, in order to circle back to the original question posed at the beginning of this posting;

“Is it possible today to use Big Data to support meaningful business decisions?”

The answer is yes. Big Data’s contributions to informed business decisions will only exponentially increase as we learn how to harness the insights to be gained. The real question is “How can your company incrementally use Big Data in your decision support processes?” Let me offer the following quote:

“I never guess. It is a capital mistake to theorize before one has the data. Insensibly one begins to twist the facts to suit theories instead of theories to suit facts.”

-Sir Arthur Conan Doyle, Author of Sherlock Holmes stories

We would appreciate hearing your thoughts and comments. All the best, Richard

Richard Sharpe

Richard Sharpe is CEO of Competitive Insights, LLC (CI), a founding officer of the American Logistics Aid Network(ALAN) and designated by DC Velocityas a Rainmaker in the industry. For the last 25 years, Richard has been passionate about driving business value through the adoption of process and technology innovations. His current focus is to support CI’s mission to enable companies to gain maximum value through specific, precise and actionable insights across the organization for smarter growth. CI delivers Enterprise Profit Insights (EPI) solutions that enable cross-functional users to increase and protect profitability. Prior to his current role, Richard was President of CAPS Logistics, the forerunner of supply chain optimization. Richard is a frequent speaker at national conferences and leading academic institutions. His current focus is to challenge executives to improve their company’s competitive position by turning enterprise wide data from a liability to an asset through the use of applied business analytics.

Why is Big Data so important?

Richard Sharpe Analytics & Big Data
 

Why is Big Data so important?

data

In my last posting, I suggested that establishing the capability to “harness” Big Data directly impacts a company’s ability to increase the generation and protection of operating profits and competitive advantage.  You could say; what’s new about a focus on profit, we do that every day?  Let’s explore linking Big Data and corporate profitability a little more.

In a recent on-line video, I was asked, “Why is Big Data such a big deal?”  I answered the question using the following scenario:

A consumer walks into a store to buy a coffee maker for his wife as an anniversary gift.  He scans the barcode for the model she wants and enters it into an app on his mobile device to see if he can buy it cheaper through another retailer.  The consumer sees that just a half mile away he can purchase the same item for 5 dollars less than the price he sees in this store.  Now, one of the retailers has set the price of the product based on a corporate margin calculation.  The second retailer knows the exact margin of that product for that store which takes into account all of that SKU’s transactional costs including the change in their fuel surcharges that were incurred two weeks ago.

Which retailer is going to win?

Companies are beginning to focus on harnessing Big Data but there is a lot of confusion regarding the “WHY”.  The attached article offers information from a Gartner survey on that very point: https://readwrite.com/2013/09/18/gartner-on-big-data-everyones-doing-it-no-one-knows-why#awesm=~ohUJBlny4JeJN4 

In my opinion, learning how to repurpose fragmented data (data that is captured within and outside of your supply chain operation) into actionable insights will be the key to innovation and future success.  Success measured by increasing revenue growth and operational efficiencies and therefore sustained increases in profit and market share.  This is further emphasized by the following quote:

“Innovation is the ability to convert ideas into invoices.” - L. Duncan

Companies that take Big Data seriously will maintain significant market advantage.  In fact, it has been suggested that the processing power of the cloud coupled with Big Data best practices will create “Disruptive” solutions to the status quo.  This is much bigger than just adding technical capabilities to process large amounts of data or to connect two different data sources together to conduct data mining.  Big Data has the power to transform the way companies will succeed in meeting shareholders’ expectations.

We would appreciate hearing your thoughts and comments.  All the best, Richard

Richard Sharpe

Richard Sharpe is CEO of Competitive Insights, LLC (CI), a founding officer of the American Logistics Aid Network(ALAN) and designated by DC Velocityas a Rainmaker in the industry. For the last 25 years, Richard has been passionate about driving business value through the adoption of process and technology innovations. His current focus is to support CI’s mission to enable companies to gain maximum value through specific, precise and actionable insights across the organization for smarter growth. CI delivers Enterprise Profit Insights (EPI) solutions that enable cross-functional users to increase and protect profitability. Prior to his current role, Richard was President of CAPS Logistics, the forerunner of supply chain optimization. Richard is a frequent speaker at national conferences and leading academic institutions. His current focus is to challenge executives to improve their company’s competitive position by turning enterprise wide data from a liability to an asset through the use of applied business analytics.

What is Big Data?

Richard Sharpe Analytics & Big Data

What is big data?

technology

We all are being constantly exposed to articles, presentations and sales pitches that have a reference to “Big Data.” Is it hype, is it real? What does Big Data mean to you and your company? DC Velocity has asked that I lead a discussion through a new blog called “Big Data, Big Deal.” My approach to writing this blog will be to author view points that hopefully offer insights and clarity into the subject. Orientations will often have a supply chain perspective given that many readers of DC Velocity are supply chain professionals. We hope you find the blog informative and welcome your comments.

As the first blog post, it is only fitting to start with our definition of Big Data. After chairing eyefortransport’s Big Data Summit in June, I discovered that there is still a lot of confusion about the context and application of Big Data. Let’s start by using a common set of characteristics often referred to as the 4 V’s:

Volume: overwhelming amount of data;

Veracity: trouble trusting the data as number of sources grows

Velocity: faster generation of data;

Variety: any type of data such as text, sensors, activities, etc.

More information can be found on the 4 V’s by simply searching the web but here is one good reference: https://www.good.is/posts/infographic-the-four-v-s-of-big-data 

However, I believe that more clarity needs to be offered, especially as it relates to Big Data and the supply chain. Today’s global supply chains are far more efficient than ever before partially due to the utilization of advanced systems that generate and manage transactions from customer demand signals all the way back to the initial sourcing or manufacturing of products (Source, Make, Deliver, Return).

Clearly the 4 V’s are at work.  However, there is an amazing opportunity to harness these Intra and Inter Enterprise transactions to gain insights and knowledge from structured data silos and unstructured datasources that can be used to directly support company centric priorities.  I often hear the argument that “our company has a long way to go to even manage the data we have today”.  But it boils down to this. Taking advantage of advances in technology and methodologies to gain actionable insights from Big Data will drive significant competitive advantage.

So, what’s the big deal about Big Data? Mastering Big Data will directly enhance a company’s capability to increase the generation and protection of operating profits and competitive advantage.

Not something you think your company can do today?  Let me close with this quote:  “Difficulties mastered are opportunities won”- Winston Churchill.

We would appreciate hearing your thoughts and comments.
All the best, Richard

Richard Sharpe

Richard Sharpe is CEO of Competitive Insights, LLC (CI), a profit contribution analytics firm that specializes in helping clients efficiently and continuously transform multiple sources of data into actionable operational insights.