Harvard Business Review[i] reported that cross-industry studies show, on average, less than half of an organization's structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or even used at all.
If you are an R&D or Engineering decision-maker, this statistic should concern you. Companies are drowning in data, yet few can leverage their data because they are stuck trying to find, access, and connect various data sources.
- How does a company get their arms around the data challenges they face today?
- How do they begin to use data to foster innovation and collaboration and gain a competitive advantage in their market?
Over the past five years of delivering data solutions to customers at Viviota, I have observed a number of data practices common among the most innovative and successful product companies. I organized these into a list of habits and will host a webinar on March 30 covering these. You can register here: The 7 Data Habits of World-Class Product Companies
The 7 habits are:
- Create a data strategy
- Adopt a Data Value Model—All data is not created equal
- Understand your data journey—from acquisition to insight and decision
- Understand how data will drive decisions
- Make thoughtful data repository decisions in support of strategy and decision-making
- Understand the role of artificial intelligence (AI) and machine learning (ML)
- Create and support a Chief Data Officer role
Habit 1—Create a Data Strategy
Just like every company needs an overall business strategy to succeed, it is every bit as critical to have a data strategy. There are many things to consider with any data strategy before we talk about what technologies should be involved.
A few years ago, I had the great pleasure of engaging with a jet engine manufacturer to study engineering data. Making a jet engine is a fascinating process. First, it has over 23,000 parts. It must operate in extreme conditions. A jet engine lasts between 30 to 40 years and is refurbished every five to seven. The common thread across the engine lifecycle is data. The team at this manufacturer wanted to prove that by tapping into existing engineering data, they could reduce overall R&D cost. The ensuing study confirmed this theory.
Some initial considerations we learned in this study need to be incorporated in your corporate data strategy, including:
- Make it someone's job at the executive level to have data responsibility as a full-time job.
- Identify the top data assets in your company and assign a value.
- Determine your data governance strategy.
- Establish high-value business impact targets that demonstrate ROI.
- Identify technology decisions concerning data that support the company's business strategy.
Habit 2—Adopt a Data Value Model, All Data is Not Created Equal
Genuinely great companies have developed methodologies to identify high-value data. They have methods to store, retrieve, and find valuable data. They know who is allowed access and who is not. They have developed standards and use data as a competitive advantage. The Value Chain of Data is a conceptual model many companies we work with have found helpful.
The concept is simple; the value of data rises as it transforms from raw data to finished. Considerations for developing and adopting a model that places value on data include:
- Creating processes and systems to enable people to find data quickly.
- Many companies get stuck at the data management layer. If your team spends more time managing and moving data rather than getting actionable insights, that is a red flag. Your organization should consider re-thinking its data management approach.
- Focus your company's resources on the highest value in the chain.
- Get your data house in order. Getting the foundation right is key to a successful data journey. Great companies focus on building a strong base
Habit 3—Understand the Data Journey—from Acquisition to Insight and Decision
Lately, people throughout the industry can often be heard saying "Data is the new oil." While they are typically talking about the power and value of data, it is also interesting to compare the journey of oil to the journey of data.
Oil needs to be discovered, drilled, extracted, transformed, and transported before it is consumed. The end consumer of oil is a significant consideration in determining its journey. Oil takes a very different journey based on how it will be used. For example, will the oil become gasoline for a car, or is it going to a manufacturing site to produce plastic?
Great companies think carefully about the data journey, its use and its value. They establish good data hygiene and consider the following habits:
- The true cost of acquiring, storing, transforming, and transporting data.
- They determine the requirements for speed of retrieval, transformation, and computation.
- They know the value of the data.
- They know who has permission to use the data and who doesn't.
- They develop standards to apply to each classification of data.
Information extraction begins in the middle layers of the Value Chain of Data model. It is here that the high-value transformation to deep insights begins. At this layer, the data has been stored in a way that can be easily searched and manipulated. In the end, understanding the journey removes obstacles and speeds the transformation to drive meaningful action.
- Establish data standards—common ways to label, store and describe data
- Establish standards for operating practices to protect and secure data which includes unexpected events that impact business continuity.
- Establish governance policies and practices for review and update.
- Establish Key Performance Indicators (KPIs) for the journey and automate.
- Establish performance metrics for data operations (for example, answer what "real-time” means for your workflows).
Habit 4—Understand How Data Will Drive Decisions
The science of making good decisions has advanced dramatically over the years. More than ever, data is being used in the decision-making process. Some decisions happen in the blink of an eye. Others evolve slowly, and time is needed to get the proper context of the situation. External factors can be an immediate or long-term driver of decisions. Psychologists have written books on the human brain’s decision-making process. One tenet stands out; no matter how much data you have, there is always a human side to every decision.
Over time AI and ML are intended to make better decisions than humans. The magnitude, scope, and speed all feed into an decision funnel. Small decisions at the base of the funnel are made quickly and easily. As the decisions get more complicated, they move up the funnel for management input. At the very tip of the funnel, the most challenging choices happen.
Too often, slow decision-making is a consequence of culture instead of a strategy. Companies often have a decision-making culture that fits with where they are on a maturity and culture spectrum. Decision-making needs to be decoupled from that spectrum to some extent. Strive for a balance during the decision-making process. Good habits for this process should include:
- Ensure timely decision-making includes both the correct data as well as human input.
- Recognize that all decisions have consequences, and each company must strike the right risk/reward balance between speed and taking action.
- Form a conscious and well-communicated strategy for decision-making within the company. Build a culture and framework to support and implement the plan.
Habit 5—Make Thoughtful Data Repository Decisions that Support Strategy and Decision-making
Today many companies have already chosen technologies to form a data repository or data lake. IT organizations have to step up to the challenging task of balancing people, processes, technology, and budget. As a result, the size and scope of data projects vary significantly by company. One of the more complex balancing acts in some industries has been addressing data in both the Operational Technology (OT) and Information Technology (IT) domains. Mainstream cloud-based database technologies offer little in the way of specific help tailored to consumers of engineering/scientific data
Industry-leading companies have embraced the idea that data is an asset. As a result, they have formed digital transformation teams. These look closely at the types of data required, the velocity necessary to process and store the data, and the transformation necessary to make the data useful. As sensors and video become ubiquitous and technologies like LIDAR and radar are deployed, existing data repositories may have a gap. Companies concerned with OT data in particular need to:
- Plan with care and foresight where data is stored and processed.
- Plan carefully where raw data is stored vs. where metadata is stored.
- Think through the security and timing implications of cloud vs. edge technology solutions.
Habit 6—Understand the Role of Artificial Intelligence and Machine Learning
Progress in the area of AI and ML is rapidly accelerating. Many companies are building entire software engineering teams to focus on specific applications of AI and ML technologies. Numerous open-source tools and mathematical models are available and can be applied to jumpstart efforts and solve fundamental problems AI and ML address.
In our experience at Viviota, one of the biggest obstacles to AI and ML success has nothing to do with technology. Addressing social engineering, change management practices, and being willing to make the appropriate investments are prime concerns. Considerations for this habit include:
- Build vs. Buy—with human software resources at a premium, it is imperative to understand what should be done in-house vs. outsourced. Ask if this information knowledge is critical to company strategy and competitive advantage.
- A top priority should be reviewing data requirements for any AI and ML project—Treat data as a high-value asset. Make sure as part of this exercise that R&D analog data is treated as a first-class citizen as well. In many cases, this data is the core IP of the company.
- Don't overlook Change Management—Resistance to change and technology adoption is a serious issue; creating the right environment to support the AI and ML strategy is critical to success.
Habit 7—Create and Support a Chief Data Officer Role
Finally, if data is strategic and valuable, and governance of data is critical, someone needs to be responsible 24x7. That someone needs to be an executive, tasked with making critical decisions and overseeing a process that leads all company elements, from R&D through IT and beyond. This is not an IT role; it is a strategic role that serves all business teams equally.
Once a CDO is identified, there will be many processes and projects to implement based on where the company is on the data maturity spectrum. A few advisory boards might be an excellent first step, along with a few small projects where ROI can be demonstrated. A few examples are:
- Data Governance Advisory Board
- Technology Advisory Board
- Data Value Project—identify high-value data and look for opportunities to increase ROI
Getting your data house in order can be a daunting task. This blog aims to provide a framework to spur thought, ideas, and a method to act on your own data initiative. All companies are different. I have had the privilege to observe many different approaches and adopt a little knowledge from them all. We counsel our customers not to try and hit a home run as they start the journey. One of the top reasons projects fail is that teams are looking for the Big Bang or the Home Run instead of taking smaller incremental steps and building momentum. It is a data journey.
In the future, I will expand on these habits and include more insight into companies and processes. I hope this short blog has spurred your thinking about your company’s data journey.
[i] "What's your data strategy?", HBR 2017