Why Data Driven Companies Outperform Companies Focused on Becoming Digital

To many in the business and technical world, the thought of going digital represents the highest form of technology. The elimination of paper, a greater collective knowledge at your fingertips, and Alexa, Siri or the computer from the Starship Enterprise there to answer your ever question or need. While consumer products have helped drive many businesses digital journeys, many in manufacturing have greatly lagged behind.?

Digitally focused companies:

Being a digital business is something very few have achieved and most will not for another decade. At best most are digital focused businesses that have started to implement capital projects specifically to capture value from additional data sources or analysis. Manufacturing companies have many challenges in their efforts to digitize. The people, equipment, existing systems, work practices, and partnerships all require evaluation and realignment. Being a digital company is not a Boolean designation. It's a continuum. It's not a destination, it's a journey. Unless and until a business has completely realized the business outcomes its defined to achieve, transformed its business and operating models to create and deliver value (and revenue) from intangible digital assets, it is not a digital business. Only the company can decide when it has transformed "enough." That being said, if you haven't started with your transformation, you are likely on borrowed time.

The term “Digital” seems to now be being used to represent anything involving computers, sensors, data collection, data analytics, robotics, automation, vision systems, process control, IT, Cloud, IOT, and IIOT. It is taken as the abridged version of “Digitally Transformed”.? Digital can’t stand for everything. When over used, effective communication would be lost because everyone would see digital through their personal filter. As a digital (digitally focused) company, are they focused on business system automation, manufacturing automation, implementing analytics, centerlining, discipline operations, robotics, remote business support, remote operations support, supervisory process controls, energy management systems, automated production forecasting, auto scale data capture or something simple like electronic operator logs? Which solution makes you digital? Does missing one eliminate you from being digital? Like many, I’m just voicing that the path is more analog than digital.

In 2019 Dan Miklovic wrote “according to the?Cambridge dictionary, transformation is: “The process of changing completely the character or appearance of something?to improve it.” However, most Digital Transformation case studies we see in the market today fail to meet this definition on one or more points.” Dan’s Three Reasons Why Digital Transformation is So Hard were:

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·???????? DEFINITION MISMATCH #1?| In many cases, the focus is on the technology, with little to no thought about the process itself. It is merely an automation project.

·???????? DEFINITION MISMATCH #2?| The second failure point ties to the expression “changing completely.” This is the most common Digital Transformation failure point. If you examine the majority of case studies today about Big Data and analytics (a common Digital Transformation theme), particularly machine learning and artificial intelligence (AI), you find that topic is about:

Dan’s Three Reasons Why Digital Transformation is So Hard were:

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·???????? DEFINITION MISMATCH #1?| In many cases, the focus is on the technology, with little to no thought about the process itself. It is merely an automation project.

·???????? DEFINITION MISMATCH #2?| The second failure point ties to the expression “changing completely.” This is the most common Digital Transformation failure point. If you examine the majority of case studies today about Big Data and analytics (a common Digital Transformation theme), particularly machine learning and artificial intelligence (AI), you find that topic is about:

• Using predictive analytics to increase reliability and reduce downtime in asset-intensive industries; or
• To improve productivity and/or quality in discrete manufacturing; or
• Reduce risk in regulated industries like pharmaceuticals.

·???????? DEFINITION MISMATCH #3?| The third way many initiatives fail to meet the transformation “definition test” is they don’t change the character or appearance of the business. After the Digital Transformation project, the business is still doing what it’s always done, just in an improved manner. So, improvement becomes the point the case studies typically revolve around. In reality, the examples so often presented today are not Digital Transformation but rather automation or evolution examples.

·???????? DEFINITION MISMATCH #3?| The third way many initiatives fail to meet the transformation “definition test” is they don’t change the character or appearance of the business. After the Digital Transformation project, the business is still doing what it’s always done, just in an improved manner. So, improvement becomes the point the case studies typically revolve around. In reality, the examples so often presented today are not Digital Transformation but rather automation or evolution examples.

With this realization, it is apparent that most companies have not fully completed their digital transformation. They are digitally focused as they continue on their digital transformation journey.

Many large manufacturers were automatically collecting data in the 1990’s, using it for analytics and root cause identification during that time frame. Industries were also working to implement/increase automation during that period. For me, the largest difference from then to now is not how or what is collected. It is the understanding of the multifaceted relationships that have always been hidden in the data. The desire to collect data is driven from the value created by it, not vice versa.

The missing piece for digitally focus companies is obviously not technology innovation, it is Business Model Innovation. Industries are overly obsessed with Productivity Improvement, Cost Savings, and Innovation through Technology (capital equipment purchases). What about Innovation through Business Model? We put technology first when we know it should be put second. Everyone is reading the same articles. There is really a lack of diversity in terms of thinking. The cultural aspect of the transformation is highly underestimated.

Data Driven Companies:

I shy away from the term “Digital company”. It is a misused attempt by many assuming that a company has completed the transformation journey. The top performing companies are data driven. Moreover, the pursuit to be fact based, but inferring continuous effort in the direction of a chosen vision. ?They have incorporated digital strategies as part of their overall response to a well evaluated case for change and high-level vision. ?I normally defer to using “data driven” when discussing the leaders in Industry 4.0 or enterprise transformation. With the understanding that through the efforts of digital transformation a company strives to free itself from the human centric design of our past. These human centric designs created great variability as individuals at every level in every decision point had independent decision rights. That automation of business and manufacturing activities enables the capture of intellectual property, data, emergency responses, and the reduction of risk in decisions from the more trust worthy data. The culture change actually has little to do with being digital. From the culture of change should be developed a competitive desire for discipline operations. Strategies for fostering discipline operations include reducing operator choice and interventions. Automation has been the most successful and consistent means of reducing operator choice and interventions.? Many companies have been far more successful automating business systems than manufacturing operations due to the difference in human engagement. Data driven companies have a case for change and strategies for improving. It is a repetitive improvement cycle.

Data driven companies act in a more structured manner. They seek data to define problems. They seek data, to measure current state, as well as establish a reasonable future state. Through the use of data to define new and multifaceted relationships for cost, manufacturing, sales and the other areas of the enterprise innovative business plans can be established. Without a business model decisions on technologies, continuous improvements and cultural advancements have high risk. To succeed, any strategy must include these three elements in sync.

Technology and business act in a push and pull mechanism. The problem is that technology is supposed to act as an enabler and business as a driver. However, for the vast majority of companies, technology is the solution to problems that have not been clearly defined. Worse yet, many companies implement new technologies without a clear strategy for the personnel impacted. Often this lack of strategy creates an obstacle to the long-term success of the new technology. Repeatedly I have observed human resources become competitive with new technology which led to a loss of the performance advancement that was targeted by the investment. The lack of a documented case for change, clear vision, and marketable strategies lead to confusion, false starts, and anxiety. We are completely blinded by many articles we read (written by “technologists”). This notion that emerging technology will transform businesses and solve every problem is saturating the public space, especially the media.?Digital Transformation should be sought as a strategy, not as a destination. It has to be part of a corporate vision of improvement. Companies must first admit they have a competitive need to change and that the solution is a continuously improving process; a system that must be kept evergreen.

Data Driven vs Digital:

“The?industrial?internet of things ?(IIoT) refers to interconnected sensors, instruments, and other devices networked together with computers' industrial applications, including manufacturing and energy management. This connectivity allows for data collection, exchange, and analysis, potentially facilitating improvements in productivity and efficiency as well as other economic benefits.[1] ?The IIoT is an evolution of a?distributed control system ?(DCS) that allows for a higher degree of automation by using?cloud computing ?to refine and optimize the process controls.” The history of the optimization focus of manufacturing provides a catalyst for the investigative behavior of a data driven company. IOT (which is the foundational driver of Digital Transformation) for a while was in bedded with the application of all new data sources directly connected to a Cloud. These segregated data sources originally fostered segregated solutions.? ???

Over the years and primarily due to IT’s influence compared to that of manufacturing Process Control (OT), IOT has taken over IIOT as the conversation piece, but these are different subjects completely. Waze would be an application that takes advantage of IOT. It very effectively uses the information from millions of drivers to help route traffic and provide a lot of other valuable information to its consumers. In Waze the data from each consumer contributes to the greater good.? There is a single analytical source and millions of people who benefit. In IIOT, the mass majority of the data is the legacy connected data. This data in many places has been used for trouble shooting and analytics for years. While the pursuit of digital transformations has called a lot of the data quality into question (not getting into that debate here) the efforts to collect and use that data provided a lot of good experience. One miss for many companies is that IOT data quite often is secluded. That is IOT instruments are installed for specific projects. The data may be routed back to the OEM or to an offsite Cloud application.? This has resulted in slow progress in full integration of that data with the legacy business/process data. In many cases, the lack of long-term vision created by a scope that was simply to deploy an IOT project was the blame. If the scope was to deploy an IIOT project (to be data driven) provisions would have been made to integrate the IOT data with the existing business and process data. For many, the solution here is to place all data into a Cloud-based storage. While that is one possible solution, it is not the only solution. The ability to capture the data and make it available for use by anyone able to create value drives the primary strategy of creating a data driven environment. The creation of data silos limits the number of employees and SME’s that can take part in diagnosing issues. The fundamental difference between trying to be digital and being data driven is the desire to automate versus the desire to leverage every source of data to drive value creation by de-risking future decisions.

Previous struggles at digital transformation

According to Blake Morgan of CMO Network, “A staggering?70% of digital transformations fail.” ?Blake reviewed the transformation efforts of three major global manufacturers:

·???????? GE created a new digital business unit but was focused on size instead of quality. In 2011, GE started a major effort to assert itself in the digital software space by building a huge IoT platform, adding sensors to products and transforming its business models for industrial products. It took the next step in 2015, when it created a new business unit called GE Digital. The goal was to leverage data to turn GE into a technology powerhouse. Despite pouring billions of dollars into GE Digital and its thousands of employees, the company’s stock price continued to drop and other products suffered. GE Digital quickly became stuck in the pattern of having to report earnings to shareholders and was?focused more on short-term goals ?and earnings than long-term innovative goals and returns.?

• Lesson: Focus on quality, not quantity. GE tried to do too much without a real strategic focus in any area. The company was simply too large to transform all at once, especially without a true vision of what it was trying to achieve.

·???????? Ford started a new digital service that was separate from the rest of the company instead of integrating digital solutions. In 2014, classic American car company Ford attempted a digital transformation by creating a new segment called?Ford Smart Mobility . The goal was to build digitally enabled cars with enhanced mobility. The issues arose when the new segment wasn’t integrated into the rest of Ford. Not only was it headquartered far from the rest of the company, but it was seen as a separate entity with no cohesion to other business units. As Ford dumped huge amounts of money into its new venture, it faced quality concerns in other areas of the company. Ford’s stock price dropped dramatically, and the CEO stepped down a few years later.

• Lesson: Integrate digital transformation efforts with the rest of the company. In this case, digital transformation as less of an actual transformation and more of a pivot into a new business area. To be successful, digital transformation needs to be integrated into the company.

·???????? Procter & Gamble didn’t consider the competition or impending economic crash. In 2012, consumer packaged goods giant Procter & Gamble set out to become “the most digital company on the planet .” The company was already leading the industry when it decided to take things to the next level with a digital transformation. However, its broad goal led to broad initiatives that lacked purpose. Coupled with a slumping economy, P&G faced problems from the start.?

• Lesson: Look at the competition. The return on investment for a widespread and expensive digital transformation was small, especially with signs on an economy on the brink. P&G likely could have seen more success if it had focused on smaller digital efforts that were more targeted to its existing products and processes. It failed to look at what was going on in the industry to see it was already ahead of competitors and what was going on with the economy. A digital transformation for transformation’s sake only won’t be effective. It must consider all outside factors and be tightly tied to strategy.

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Blake’s article highlights that most failed transformations show common missteps. Once the roadblocks where recognized all three companies where able to pivot and execute successful transformation strategies. Data driven companies are focused on being better companies as opposed to being the company that uses the digital solutions the best or the most. The fact-based focus of data driven companies helps to ensure they have aligned strategies guiding their transformation efforts.

Data driven environment versus an analytical swot team. ?

Most data driven companies achieve a point of enlightenment. Manufacturing moves in the direction of being less magic and more science. Fewer unplanned events happen and more importantly very few issues go unexplained. Predictive analytics are applied to procurement, sales and business trends. ?The debate here comes down to continuous improvement compared to a hot market activity. That is not to say that there is any issue with IOT, the emotional response to hire data scientist in order to try to capitalize and remain competitive, or the drive to automate. A sound vision and strategy will have data scientist, data driven leaders, and data driven employees that will be able follow the analytics and science. One opportunity for IOT is quite often many of the human resources are left behind in hope of a magical easy button. Confusion and at times a lack of knowledge is often mistaken for resistance. For many, the scope to digital is assumed to be to move the on-premise data to a Cloud service and onboard a department of data scientist. Unfortunately, this course routinely ??leads to poor acceptance and slow progress.

Summary

Digital focused companies:

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·???????? Local experiments lack connection to a corporate vision and strategy to address specific large-scale gaps

·???????? Improvement effort is dependent on a small percentage of the company employees

·???????? Expansion of successful experiments is slow. Often experiments are repeated due to a lack of trust or knowledge of previous results

·???????? Technology and IT focused

·???????? Highly swayed by vendor guidance

·???????? Highly capital dependent for improvement

Data driven companies:

·???????? C-suite alignment to corporate vision that guides the corporate strategy of continuous improvement

·???????? Focus on improvement includes increasing value created by human resources

·???????? Experiments are implemented to identify opportunities to create value based on the strategies to address the identified large-scale gaps

·???????? Successful experiments are rapidly repeated and optimized based on KPI

·???????? A single authority for data is established to create a single point of truth and trust

·???????? Internal and external human resources at all levels are encouraged to engage with data to identify improvement opportunities and to resolve problems

·???????? Optimization efforts are continuously monitored and routinely updated with new goals based on the revised gaps to competitive and performance goals

·???????? Value creation is sought universally throughout the enterprise.

·???????? Data analysis and automation are integrated with LEAN techniques and leveraged to encourage disciplined operations applications

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?Elliott Bell

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