The quick Data revolution we are experiencing is changing businesses and industries deeply, making leaders to slow down the pace to adopt this rapid transformation because of lack of capabilities. A Data Culture fast-tracks the development of Analytics to support the enterprise management and leads it apart from any uncertain-end result.
An open mind and perseverance are needed on the transformation to a new framework to recognise the useful benefits driven by a synchronised change of culture in talented individuals and decision making. What can we do to drive trust in our organisation and quickly adapt to new changes?
Seven useful practices which can help you in this journey
1.Data Culture process: Determine the policies allowed or not, when agreeing access to the data. The expectation is that if someone does get the data, it would be treated effectively, and if it is transformed, there is need for controls.
The primary goals:
Collecting, analysing, and deploying data to make better decisions. The best advice for senior leaders trying to develop and implementing a Data Culture is to concentrate on the results and the business objectives. A large amount of data is not a useful Data strategy. Find those business problems and commit your data-management efforts toward them. Work on it rapidly and send it back to the team or the customer; use the insights, ideas, and innovation generated by the group or your customer as a fast-track for improving the capability of your product and services, and provide the information they need to do better jobs.
2. Data culture, CEOs and the board: Feedback is essential, not only to listen but to share it. Do not waste time! Focus on the more significant things, communicate in a quarterly basis what has been done, the challenges, time spent, but above all, the resulting value when you see it, a “well done” is always welcome, as it encourages the team to go on, because he/she now understands the value of it.
Commitment from CEOs and the board is essential, but it is as crucial as it is from the team. Data is the life force of organisations; therefore, as more people digitise their tasks, the better you gain in transparency and access to that data in a way to deliver value. If everybody sees what everybody else is doing, then the great ideas tend to rise to the top. Information needs to be treated respectfully, with people taking care of what they put in or modify.
3. The democratisation of data: when adopting a new mindset of Data Culture, you have to figure out how to democratise the Data Analytics capability. You need a portfolio of projects, the ones delivering value in real time to your company, and make it available across a platform into which people can easily access data broadly. Eventually, people start believing in it and feel free to deliver solutions that don’t require a costly Data Scientist. This process of trusting on Data brings a change of behaviours, based on a new understanding of all the benefits embedded into the systems and procedures, and because your team now can proceed on their innovative ideas to create value (locate the most costly scientist in the most significant problems.)
4. Data Culture and risk: the certification requirements for software embedded on our products is huge! The use of Data Culture helps us understand exactly what they’re doing, so that productivity and safety go hand in hand. Data has to be treated safely and effectively; if it is transformed or moved, make sure it’s with the controls in place. We have to comply with lots of rules and regulations across the globe before implementing the decisions correctly.
5. A Data-Culture “interpreter”: we require people who can bridge both Data Science and on-the-ground operations, but once again, there is a significant shortage of specific-skilled talent. In spite of this, you might have in your company the skilled player who takes the challenge of adapting the Data Culture process, and tailor it for the coming years. A knowledge individual to lead the implementation of the new process, conceptualising ideas from high management, as well as operating the execution of it. Bring him along to become “the interpreter” who interacts with CEOs or teams, train others, give feedback, organise meetings and establish a continuing-education workshop.
6. Protecting our Data: we can create unique visions, ML and AI algorithms, applications and software products for our teams, by transforming our operations to serve our customers much better; to accomplish it, we need to find talent among our co-workers as to build those capabilities in-house and rely less on outsourcing. All projects could also go into the Cloud; it's beneficial that we have a process in place to do that. Our cybersecurity awareness is to put the frame in place to protect the company.
7. Pair talent and culture: the competition for Data talent is on the growth. So, it put us in the position to appropriately balance our company between bringing new employees and re-educating existing ones. Focus on those talents you can insert into your business, who can support you to efficiently execute the transformation, but also your pipeline of the leadership team in the future.
CONCLUSIONS: You don’t need to have a PhD in Computer Science. Look for the person who is remarkably good at working with data and creating value from it; focus on their ability to learn the business, manage products, interact with clients; people who bring innovative ideas to the table, secure with risk and regulations, active with technology, and with business processes. You want somebody who can communicate effectively, both in writing and orally, with some subject-matter expertise.
Technology and culture have to match. Data Culture can be a problematic, but when excitement about Data Analytics inspires the entire organisation, we can live through an exceptional technology and culture match. Start now!
Dave Food
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