“Who’s” on First, “What’s” on Second, “I Don’t Know’s” on Third

Richard Sharpe Analytics & Big Data
 

“Who’s” on First, “What’s” on Second, “I Don’t Know’s” on Third

Many of us remember the great comedy routine by Abbott & Costello Who’s” on First, “What’s” on Second, “I Don’t Know’s” on Third.  If not, take a look at this YouTube link for a quick laugh:

 

So what does this have to do with Big Data and the value that can be derived through meaningful business analytics?  Well just about everything.  In the Abbott & Costello routine, the names of the players on the bases are creating miscommunication and confusion.  The same confusion can often be seen when companies are trying to derive meaningful insights through Big Data initiatives.

Big Data initiatives that create “One Version of the Truth” are more successful in driving strategies that are actually implemented and that have significant financial impact.

Here are three considerations to help drive One Version of the Truth to achieve success and the realization of significant financial impact through the use of analytics and Big Data:

 
  • Cross-functional participation is critical.  This is especially true in defining the objective (intent of the effort), reaching agreement on how the Big Data is going to be used (repurposed), determine the sources of the data and the data validation process.  Cross-functional consensus early in the process can help eliminate the analytical results being questioned and or dismissed. Without cross functional participation, you minimize your chances at finding One Version of the Truth and achieving success.
  • Assuming the result you are seeking is an ongoing need of the business, it is important to recognize early on that your Big Data approach must be sustainable.  This means that the sources of the data, the rules on how data is going to be used and the way that validation occurs are repeatable and consistent.  Not giving this the right level of priority is a sure way to lose the organization’s confidence in the ongoing results. You don’t want to create One Version of the Truth once, you need to be able to create it every time.
  • Big Data initiatives need to be very intentional with regard for the desired result. Hoping to find those nuggets of gold by wandering around a landscape containing masses of data is futile.  One Version of the Truth eliminates turf battles and highlights not just nuggets but veins of gold.

Naturally there needs to be flexibility to make changes but ensuring that the above considerations are applied is critical.

For those that don’t agree, consider the following: how many times have you been involved in a project where the work that was done was solid but at the close of the effort someone objects to the results?  Their argument is that they don’t trust the data that was used or agree with the analytics approach that was taken.  Yep, one step forward, two back and then the nail in the coffin.

No one wants to be associated with a career ending project that fails.  Following the three considerations above can help.  Otherwise you may find yourself answering the question from others in your company “Who’s on First”?

I 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.

Show Me The Money – Building Organizational Momentum

Richard Sharpe Analytics & Big Data
 

Show Me The Money - Building Organizational Momentum

Continuing the theme of Big Data initiatives need to be very intentional, we now turn our attention on how to build momentum by focusing on examples of how companies are financially benefiting from their successful efforts with Big Data.  Specifically, let’s talk about three areas that are getting a lot of attention in many companies.

Product Segmentation – having accurate insights with regardto how a particular product is performing with regard to contribution to profit can be eye opening.  Forget the expected “bell-shaped” distribution curve and expect to see more of a skewed left hand spike (left is negative or marginal behavior) and a prolonged tail of the curve to the right.  Couple this with where the product falls in its product life cycle and you have the tools necessary to drive actionable steps to dramatically impact financial performance.  This has been demonstrated in multiple industry with astonishing results.  You may be saying, “Yes but we have to have product x to sell product y”.  That may be true but what if the customer is “cherry picking” and you actually have an opportunity to identify where that paradigm just doesn’t hold water.  Getting to these forms of actionable insights builds excitement and momentum for your Big Data efforts.

Customer Segmentation – talking about skewed bell shape curves, how about having over 50+ % of you customers performing marginally or continually being unprofitable.  Want to fire them?  Well possibly a better strategy would be to understand why they are unprofitable vs a minority of similar type customers who are contributing to the bottom line.  A better strategy for some customers may be to determine how to get the marginal customers to emulate the behavior of the well performing ones.  Moving away from a “one size fits all” customer strategy can offer significant increases in profit contributions.

Operational Realignments – no, we are not talking about a large, time consuming optimization project that will be difficult to accurate monitor the realized ROI due to unforeseen changes in the future.  In fact, it is just the opposite.  Identifying very specific and target changes using Big Data can yield impressive results and have the organization want to do more hunting for operation improvements.  A recent example was the changing of certain inventory stocking decisions tied to customer demand yielded not only higher service levels but cut millions of dollars of cost out of the operation.

The above are just a small sample of how companies are driving increased momentum for their quest of improving financial performance using Big Data.

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.

Creating A Success Framework – 5 Steps To Follow

Richard Sharpe Analytics & Big Data
 

Creating A Success Framework – 5 Steps To Follow

In my past blog posting, I continued to highlight the need for Big Data initiatives to be very intentional in their focus and execution and suggested that there are three main success drivers.  In this posting, I will address the second driver which is following 5 steps that increase the chances of success.

The five steps that will increase your chances for having successful Big Data initiatives are:

  1. Ensuring ongoing Senior Level Sponsorship
  2. Gaining and maintaining organizational consensus
  3. Taking an Enterprise wide perspective
  4. Implementing a rigorous data governance process
  5. Obtaining repeatable and measurable impact

It would take too much space to discuss each of these steps in detail but I will offer specific points for each one.

Ensuring ongoing Senior Level Sponsorship – like any important initiative, the organization needs to understand that the Big Data initiative underway is a high priority for the organization.  This message needs to be continually reinforced and tied to the expected value it holds for the company.

Gaining and maintaining organizational consensus – recognizing the significant financial potential of making fact based decisions using Big Data insights can be very motivating.  However, nothing can kill the initiative faster than lack of buy-in to the results from those organizations not involved from the beginning.  Yes, it takes more time to gain consensus each step of the way but in the end you will cross the finish line faster.

Taking an Enterprise wide perspective – one of the biggest problems that companies face is the fact that so much of their data is held in “functional silos”  that are not easy to connect.  To be most effective, Big Data initiatives need to utilize methodologies and technologies that allow for data from disparate systems to “play well together”.

Implementing a rigorous data governance process – if the organization does not have confidence that the way the Big Data is being organized, validated and used consistently over time, you are basically wasting your time.

Chart_BarObtaining repeatable and measurable impact – people like to get on the bandwagon of success.  Celebrate and make visible the early wins from your Big Data initiative.  In addition, track the performance gains over time to continue to reinforce to the organization the value of fact based decisions using insights from Big Data.

Following these five steps will help ensure the success of your Big Data initiative.  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.

The Journey – Understanding The Path Of Adoption

Richard Sharpe Analytics & Big Data

 

The Journey – Understanding The Path Of Adoption

I recently facilitated the Big Data track for the EyeForTransport (EFT) Conference last month. As part of that conference, I emphasized the need for Big Data initiatives to be very intentional in their focus and execution. I also outlined three main success drivers: (1) recognizing that the road to success is a journey, (2) following 5 steps that increase the chances of success and (3) using other company case studies and their financial wins to build organizational momentum. This posting will address the first point.

There are three stages of adoption for business users to master Big Data. It is important to recognize this fact very early in the process. I often have conversations with customers who feel that they need to focus on getting the most out of their Big Data initiative as quickly as possible. This mindset can be detrimental. It can set expectations that later do not materialize and therefore the overall effort loses credibility. However, the smart approach is to establish that the company will incrementally build on Big Data opportunities with each stage in the journey providing significant value.

Let’s elaborate. One of the initial benefits of mastering Big Data is to provide operational insights that you just could not get before. Said another way, “discovering what you don’t know.” This is why it is so important to focus first stage activities on intentionally enabling business user to learn as much as they can through discovery. Using these insights to make informed decisions yields more effective strategies which of course builds organizational support for your Big Data initiative.

Knowing that you will continue to have these insights will naturally question if certain business processes could be modified to continually take advantage of this new knowledge. This provides additional opportunities to improve company performance. Over time, questions will begin to surface on the “go to market” approach for running the business which will drive innovation. All three phases offer significant value and continue to build internal momentum.

Blog009_3StagesOfAdoption

Mastering Big Data is a journey. Recognizing that point and continually reinforcing this message to your organization is essential. 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.

Designing Solutions for Business Users NOT Data Scientists

Richard Sharpe Analytics & Big Data

Designing Solutions for Business Users NOT Data Scientists

Big Data Analytics

There are a lot of opinions with regard to how insights should be gained or “mined” out of Big Data.  Clearly, no one has all of the answers today.  However a popular position is that companies will need to hire a team of data scientists to actually derive insights from Big Data.  I take a fundamentally different position on this for a number of reasons but the main headline is that effective solution that use Big Data need to be designed for the business users, not data scientists.

To make the point, let me take us back to my childhood years when we would play the “whisper” game.   We would line-up side by side, someone would have a phrase or saying in mind and they would whisper it into the ear of the person next to them.  The process would continue until the last person would have the phrase whispered to them and then they would say it out loud to the entire group.  How often do you think the original phrase matched the one that was announced to the group?

The same is true for making Big Data actionable. One approach is to hire data scientists to begin to organize data and to use various data manipulation and evaluation techniques to look for patterns and insights. These discoveries can then be provided to business users for review and possible use.  This may work well for some enterprises but having people who do not have the business knowledge try and discover these relevant insights seems problematic.

I recently ran the Big Data Track for the EyeForTransport (EFT) Conference in Chicago.  As part of that conference, I emphasized the need for Big Data initiatives to be very intentional in their focus and execution.  In future postings, I will elaborate on the three main points for the framework for driving success in using Big Data.  But the purpose of this posting is to emphasize that effective solutions using Big Data should be intentionally designed with the Business User’s needs in mind.

Big Data and Advanced Analytics are converging at a rapid pace.  Putting these solutions in the hands of Business Users will drive insights that drive intentionable performance improvements.

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 4: Creating Repeatable and Measurable Impact

Richard Sharpe Analytics & Big Data
 

Turning Big Data From A Liability To An Asset – Point 4: Creating Repeatable and Measurable Impact

reduce SKUs by 20% or more

This posting is the final in a series focused on turning Big Data from a liability to an asset. Earlier in the series, I have discussed the importance of gaining Organizational ConsensusTaking An Enterprise Wide Approach and the need to have effective Data Governance in addressing effective ways to get value from your Big Data initiative.   I hope these areas of focus have been helpful.  Today, we will focus on Creating Repeatable and Measurable Impact.

To effectively tackle Big Data, the strategy must be intentional with regard to the business need that the effort will support.  All too often I hear someone suggest that the way to address Big Data is to hire a few very smart data scientist, put them in a room with access to enormous amounts of data and see what they can discover.  I totally disagree with this approach.  Sure, they may find interesting business patterns or correlations but deriving financial value from the effort may be difficult.  Instead, a far better strategy is to focus your Big Data initiative on a specific business strategy(s) where the insights gained from Big Data can be used to make actionable decisions that can be tracked with regard to a measurable improvement in the business meeting its objectives.

Tackling Big Data requires the investment of time and resources.  Like any business investment, the initial justification for tackling Big Data needs to be financially justified, but getting value from Big Data in an intentional way is not a one-time event.  It needs to continue to provide value that can be measured in meaningful ways to the organization.  Without that, it is purely a waste in time.

So the question is how.  Measuring value can be a tricky task but it cannot be ignored and pushed out to be addressed “after the fact”.  Like any measurement, the first step is to verify you have a Baseline “value” for performance that should be defined and cross functionally agreed upon. It is also important that the way you calculate the measurement has been scrutinized and validated, so that it will not be questioned later by the “Doubting Thomas” members of your organization.  All of this should be done prior to taking action that is directly linked to the insights you gained from your Big Data initiative.  Finally, it is key that the ongoing use of the measurement of value is consistently applied each time it is used.

These are all standard value measurement considerations but they are just as important for sustaining your Big Data initiative as they are for any other business investment.

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 3: Data Governance

Richard Sharpe Analytics & Big Data
 

Turning Big Data From A Liability To An Asset – Point 3: Data Governance

data governance

Big Data is something that companies are trying to define with regard to what it means to their operation and to their competitive landscape. When considering the growing number of sources of unstructured data (e.g. social media) and structured data, just defining the landscape of what you are talking about can be difficult. In this blog we have provided a framework for how to define Big Data, getting value from Big Data and now providing actionable points on how to turn a three headed monster into something that adds significant and ongoing business value.

In an earlier posting we identified the requirement to gain cross functional consensus with regard to how Big Data solutions are created to serve the intended purpose of solving a business problem(s). We also focused on why it is so important to take an enterprise wide perspective to maximize the value of the investment. In this posting, we will focus on the importance of data governance.

What does data governance have to do with Big Data? Everything. Effective solutions take time and resources to build correctly. The question is do you want that investment to solve a business problem one time or to continue to support solving the business problem over time.

Problems are solved by business leaders and managers making decisions that positively impact the efficiency and financial impact of the operation. Decisions that are fact based and that are actionable.

Ok, so what does that have to do with Data Governance? Let’s say that you have a significant business problem to solve. The information that you have in front of you is known to be consistently accurate and specific to the problem area. This is a function of the information coming from the same source, that the information has been verified by Subject Matter Experts (SMEs) and the way that it has been processed is consistent to provide the information you need. What did I just describe? Data Governance; the insurance policy for Big Data, and this insurance policy continues to provide returns as you measure the impact of those decisions over time.

Data Governance provides the rules for which you are obtaining, organizing, validating and processing the vast amounts of structured and unstructured data to gain competitive advantage. Without it, your Big Data investments are a waste of time.

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 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.