Data and Innovation: Leading With and In a Data World
Today’s world and future is built on data.
Interconnectivity is now key to business transformation. Online activities of businesses and consumers have been growing in an exponential manner. This has led to the creation of incredible amounts of data. It is evident that companies are making use of customer data and data extracted from their production process. While it is obvious new entrants have dominated in a short time leveraging data, the implication of using Big data cannot yet be fully examined.
The more advanced data processing systems become, the better is the output. Big data can improve business process, decision making and help in creating innovate products. As earlier mentioned, new entrants are dominating leveraging data, financial worth is growing exponentially and they have become choice companies for employees and job seekers.
In this article, I will be highlighting how industrial and heavy equipment companies can leverage data to better performance and remain competitive in a data world.
How to Lead with Data:
1. Build Trust: If stakeholders and employees don’t trust their data and models, they cannot trust any decision that will be made from data. Trust is a defining factor in an organization’s success or failure. Indeed, trust underpins reputation, customer satisfaction, loyalty and other intangible assets. Trust inspires employees, reduces uncertainty and helps build resilience. Trust is anchored on four attributes.
I. Quality: Data gathered and model used need to match context in which the insights will be needed. Many times, this starts with questions about the quality of the underlying data and as analytics become more sophisticated and machines start to do their own learning, quality also extends to the models and algorithms.
II. Effectiveness: Effectiveness is the extent to which models achieve desired results, providing value to decision makers who rely on the generated insights. When analytics are thought to be ineffective, or are used in an inappropriate context, trust fades away quickly.
III. Integrity: In a data world, integrity refers to ethical and acceptable use, from compliance with data privacy laws to less clear areas such as the ethics of profiling and predicting behaviors. This anchor is a growing concern of consumers, and it is rapidly becoming a key focus for regulators and policy-makers, as they strive to assess the ‘fairness’ of analytical approaches.
IV. Resilience: Resilience is about optimizing data sources and analytics models for the long term. Cyber security is a well known example, the constant change in digital infrastructure need to be a priority consideration. This kind of resilience is particularly important as analytics become self-learning and reliant on one another, using integrated algorithms to acquire input data.
These anchors are a useful framework for assessing the level of trust that users place in analytics and will help in application of recommendations in building a robust trust program.
2. Create Technical Innovations: Innovation is key to remain competitive. It is crucial to the continuing success of any organization. To lead, application of practical knowledge is non- negotiable. Industrial companies should leverage past experiences to develop new solutions to problems. No one is a winner in the data world. It is too big to handle alone. Thus, collaboration is very vital. Companies should strive to increase product quality via technical innovation, influence standards by results achieved, pave the way, and gain recognition by participating in a globally recognized ecosystem. Just like any company that ignored the Internet at the beginning of the century, the ones that refuse innovating leveraging data risks getting left behind.
3. Ride on The Wave: Data has created a higher level of energy, control and diverse perceptions in the business world. Today organizations are seeing lots differently. Relationships that never existed before now do. Competitors are now partners. Who would have thought that General Electric would become a software company someday but then change is the only constant in business. There is lot to benefit from the momentum data has and is building in the market place. The heavy equipment companies need to rigorously attend to Proof of Concepts (PoCs). The PoC gives an assurance a solution can deliver value. Feedback is crucial for success, the PoC helps provide feedback about a promising solution, while reducing unnecessary risk and exposure and providing the opportunity for stakeholders to assess design choices early in the development cycle. The velocity in the data space is hard to measure, thus the need to work with system of partners. It is however important that heavy equipment companies see to credibility and validation. Where there is high momentum, there is noise also. It is important to seek the right partnership.
4. Raise Thought Leadership Status: Leaders are listened to and are great listeners. They play a major role in the future of any technical or technological innovation. The heavy equipment companies need to have a strategy to be the “go to” when it comes to an area of specialization. For instance, the General Electric Predix is a go to platform for oil and gas IoT applications. To achieve a great thought leadership status, companies should have a Centre of Excellence (CoE) with the Clevel buy in. This will help produce deep research, define “all” customers’ challenges and best way to overcome them. Invitations to define best practices should be welcomed.
5. Develop Testbeds: In raising thought leadership, research is crucial, Testbeds provide a platform to perform rigorous testing of the outcomes of research carried out. Through testbeds heavy equipment companies can find an immediate array of ecosystem partners with product technology and expertise. Testbeds provide the opportunity to control the direction of data technologies, it helps in defining standards that guide the development, production and use of a particular data solution. In leveraging testbeds to lead in the data world, the Industrial companies should;
- Explore untested data technologies.
- Initiate innovation and vigorously test Proof of Concepts (PoC’S).
- Influence standards and drive multi user interoperability.
Call to Action! (Champion/s Needed)
Someone has to take this on. Every company transformation starts with a leader. Your leadership is required, even if it is only to find someone else who can lead this effort. Most companies are struggling with the same set of issues that slow down progress towards a truly effective data strategy. Make sure that your company takes the steps necessary to move ahead with the imperative to lead with data. You may need some external help to get this transformation accomplished. Cedar Analytics is happy to work with you getting started.
References:
https://www.iiconsortium.org/pdf/IIC_Industrial_Analytics_Framework_Oct_2017.pdf
https://www.pwc.nl/en/publicaties/predictive-maintenance-40-predict-the-unpredictable.html
https://assets.kpmg/content/dam/kpmg/xx/pdf/2018/02/guardians-of-trust.pdf
