The exploitation of large amounts of data has the potential to fuel a new era of innovation for companies. Business leaders now have the backing of plausible facts when it comes to making decisions and defending new ideas.
Driven by competitive pressures in the industry and the need to streamline operations and redirect strategy, companies have over the last decade accumulated data, invested in technology and generously paid for analytical talent.
However, for many companies, a strong, data-driven culture remains a conundrum, and data is rarely truly useful in decision making.
Key challenges in creating a data culture
Most of the problems that companies face in integrating a data culture into their core business are not about technology, but about the culture itself, data literacy and the change in mentality that this implies.
The success of data and analysis is in becoming a business driver of value creation. There are a number of common factors that prevent data and analysis leaders from generating this value:
- The lack of active management standards.
- Lack of experience.
To achieve success in data and analytics leadership, we propose to apply 10 practices to ensure effective data-based transformation in your company.
10 key steps to achieving a data-based culture
Proper preparation, assessment, planning, performance, measurement and, above all, communication must be applied to each of the practices outlined below as they will greatly improve your chances of success.
1 – Have a robust and mature data technology. Does your organisation have a reliable process to integrate data? Without the support of a big data and analysis platform, you’ll be blind. Invest in developing technology expertise to guide and support you in the process.
2 – Be open to different sources. Take advantage of alternative external data sources and combine them with internal sources to obtain multiple data sources. It is key to find out what other organisations and industries are doing with the data. Without that awareness and thorough research, you will not be able to broaden your approach and achieve the level of innovation that you should.
3 – Achieve data flow. It consists of having all the data in a single point that allows employees and departments to connect and make use of it. Transparency, fluidity and ease of connection will bring value to all departments. This is achieved by standardising data, processes, tools and even terms. In this way, information will flow transparently in a more effective way and employees will become familiar with the most fundamental part of the data: Where it is and how to use it properly.
4 – Do not isolate scientists from data. It is necessary to expand the functions of this department to the whole company. This requires the creation of new business competencies and a direct relationship between the different parties. The CDO (Chief Data Manager) must become a problem solver with both a business and a data and analysis perspective. One example is to associate the CDO with the CFO (Chief Financial Officer) to formally value the information assets of the organisation and therefore improve the management and benefits of the data.
5 – Addressing the impacts of cultural change from a data-based approach. This must happen from the beginning, so that the team understands that change is necessary. You must be explicit about how data influences different decision-making styles and communicate the benefits to employees. Benefits such as saving time, helping to avoid duplication of work or getting the information needed frequently will motivate them and make them more proactive in dealing with it.
6 – Data literacy capacity. A necessary step is to set up specialised training at all levels of the company so that reading, working, analysing and discussing data is not a problem. All employees must be able to ‘talk’ with data. Leaders involved in decision making need to go further, have knowledge of coding and be conceptually fluent in quantitative issues, especially leaders involved in strategy, who need to internalise the expectation that data assets are there to be shared.
7 – Identify and communicate the commercial value of the data. The information must be measurable in order to be evaluated. Metrics must be carefully chosen taking into account the nature of the data and the final objective of the data. The information should be treated as if it were an asset. To do this, it is necessary to have much stricter control over the origin and consumption of the data.
8 – Address the ethical implications of data and analysis: Both internally and externally, a code of conduct is needed that defines ethical guidelines for the use of data and analysis. Take time to balance the benefits of data and analysis with the ethical and privacy risks involved. Knowing the expectations of trust of all parties involved will help you keep the balance.
9 – Solve basic data access problems. By far the most common complaint from employees when it comes to obtaining even the most essential information. Don’t let this happen at this stage and make sure that access to data within your company works as it should. Data democratisation is the key to making decisions based on tangible, easy to understand, business-focused data.
10 – Traceability of uncertainty levels. Require teams to be explicit and constantly report their levels of uncertainty in a quantitative way. This will enable managers to deal directly with potential sources of uncertainty and to rigorously assess the reasons for acting on them.
A data-based business cycle requires three capabilities that we have integrated into the list of steps: technological capability (data creation and integration), data fluency (business intelligence) and data literacy (decision management). These capabilities must be worked on in a strategic path that designates the right competencies and rebalances work to be in line with objectives.
A global data strategy
Hiring a team of engineers and data scientists to let them experiment on their own will not yield the results that many managers expect. In order to extract real value from information sources, a data-based company must be created, not a department. The first thing is to develop an enterprise architecture aligned with the business vision, which will serve as a base model and will be developed further.
The digital transformation is a human process. Therefore, there is a need for a comprehensive, data-driven education and mainstreaming program that is measurable, customised, affordable and rapidly scalable. The foundation of this program will mean improved quality, reliability and access to data, and continuous monitoring of the journey and analysis of both systems and people is essential to its success.
If you are looking to create a successful data based culture, count on Nexus Integra: A software that manages and controls all the data generated by the different production processes and displays them in the most intuitive way, always adapting to the needs of your company.
Nexus Integra unifies all the equipment, processes and people in a single integrated operations platform. You will be able to visualize on a world map all the integrated plants and navigate through each one of them in a quick and easy way with just one click, as well as compare data from different plants or lines.
Contact us and let’s talk about how Nexus Integra can help your company.