Business priorities should inform data priorities that have specific and quantitative objectives. These priorities should drive the planning, execution, and adjustment of any data strategy over time. Before committing to a data-driven goal or project, document clear strategic objectives and expected outcomes. If a business objective is to reduce operating costs and maintain current prices in a competitive market, the data project objectives would look like "Reduce scrap rates by 20%" or "Increase production capacity by 10% ". These are clearly defined and easily measurable objectives. Culture is another aspect of strategic alignment. For companies focused on tradition, conventional practices, and security, the data strategy should align with that culture and only adopt widely tested practices. If the company is driven by innovation and disruption, it is more likely to take an early adopter approach to data technology and iterative data strategies. In addition to efficiency goals, data strategy also supports strategic growth goals, such as expanding a service portfolio or improving product quality. The eight value drivers for Industry 4.0, as defined by McKinsey, provide a good reference for industrial companies:

1. Where to generate value with data

2. Service/after-sales

3. Resources/processes

4. Asset utilization

5. Work

6. Inventories

7. Quality

8. Supply/demand adjustment

9. Faster time to market

Developers and system administrators work with data specialists, but they generally do not have the training for advanced data-related work such as building ML models. Before embarking on a data-driven transformation, a company will need to train existing employees, hire new positions, or partner with a vendor that can fill the roles. These roles, such as data analyst or data scientist, are difficult and expensive to fill given the job market. Many organizations opt instead for a partner-supported approach. This option ensures scalability as requests for new data initiatives grow or a data strategy is deployed to new geographies, sites, teams, etc.

End users must also be prepared for data-driven change, and their skill level must be incorporated into any data strategy being developed. Important questions for this evaluation are: Are users prepared to use the capability? Are you willing to embrace this change and see the value? Does this solution address specific needs expressed by end users and advance our strategic goals for end users? The answers to these questions will guide the implementation of new capabilities and inform how much training and education to plan.

This question has several aspects. First, a common question is: "Is it better to use a proven solution or one with the newest or latest technology?" The company culture will guide the level of risk and innovation to be achieved, and will help identify which suppliers best fit the company culture. Working with the internal team and other vendor partners is essential to the success of the data strategy. If there is a mismatch in work styles, risk tolerance, and other attributes, projects may take longer than planned and won't run as smoothly. Scalability is another important consideration. Having scalable technology systems for data engineering ensures that data services can scale as demand grows, as capabilities evolve and become more powerful. Look for an open architecture with seamless accessibility that is proven in your industry. This will reduce the risk of becoming obsolete with systems with evolving needs.

This is an important question because it can delay optimal benefits from data strategy investments. The entire operation does not need to be in optimal condition before using advanced analytical tools. Rather, limit pilot or early data initiatives to areas and processes that are already working efficiently because:

A.

If a process, team, or department is not working efficiently, focus on optimizing operations using more traditional tools first. Using technologically sophisticated data tools to solve basic challenges can be a waste of resources and time.

B.

If a major problem is discovered during a data project, it is common for the project to focus on the major problem. This can derail the data strategy, delay project timelines, and decrease needed support. It is best to minimize surprises with a clear analysis of current operations.

Aim for success from the beginning. Rushing into a transformative data strategy without the proper foundation can limit the resulting benefits, even if sophisticated data tools are available for operational roles. Use these four questions above to take a check on your current data strategy or plan your next step towards a more advanced strategy. Taking the time to get things right at the beginning will help projects run on time and deliver the expected results. The PI System™ is an enterprise platform for your operational data that can help advance your data strategy. It allows you to collect, analyze, visualize and share large amounts of high-fidelity time series data from multiple sources with people and systems across operations. It offers a framework to help you contextualize information and make better business decisions, and make any new data strategy more successful.