Data is critical to a better customer experience, more efficient operations, and new revenue streams. The organizations that best analyze the data will be the most competitive and effective. As a result, companies are turning to so-called “intelligent” analysis technologies such as artificial intelligence, machine learning, natural language interactions, and complex algorithms to gain a lead and improve their analytical skills. drive organizational change and catalyze the digital transformation of their activities.
But only a few of these promising technologies have prevailed so far. In secret and hype, they rarely reach us through specialized data workers. Because of their underlying complexity, the focus remains on technology itself, not on how ordinary people deal with it and benefit from it.
In addition to simplifying the analysis, we must focus on the importance of trust. Users will not use Smart Analytics if they do not understand and believe it. Only through trust can we achieve the kind of mass availability and transformative change that is possible through intelligent analysis. It starts with the basic trust in the value of the data and the technologies that surround it. We can then help our employees understand how they can best use these smart technologies to improve their productivity and knowledge.
Create an agreement on the value of data and information
It is extremely important to get people to choose a data-driven approach to integrate smart technologies into their business. People need to believe that data is essential to the value and success of the business, and that companies that are better equipped to better use their data outperform companies that do not. If the use of data resists decision-making, there will be obstacles to new technologies that facilitate the analysis.
How do you create a culture of analysis? First, focus on making data available company-wide. Provide the analysis at all levels of the organization and emphasize the importance of making all decisions based on the data. Increase your behavior by integrating data and analytics directly into decision-making and answering questions in real time. Measure the use of the data. Understand its effects. And builds a community that evangelizes them, including supporting the executive to strengthen their meaning.
Demystification of Smart Analytics
Often people avoid what they do not understand, and they hate to look stupid by not understanding something. We need to show people that most of us do not really understand intelligent analysis. This is a relatively new area and we are still learning. Education and transparency are the key to greater trust.
As algorithms and models become more sophisticated, it is important that they do not become incomprehensible. The concept of “explainable AI” is very powerful – I should be able to understand the operations and logic used to find an answer. This helps me to develop my belief that the answer is correct. Artificial intelligence techniques need to reveal their inner workings and help to recognize and avoid the bias people bring to the analysis. This combination makes the best of both worlds: the man and the machine.
Help users to use Smart Analytics instead of replacing them
People will not trust anything if they think it will be life threatening. With intelligent analysis technologies, the opposite happens! Users should see Smart Analytics as a way to improve their performance rather than replace it with a threat. Together, we need to eliminate misconceptions like “artificial intelligence will replace my work” and show people how machines learn from data, not experience. Smart analytics can help employees make better decisions, increase efficiency, automate, personalize, differentiate, and much more. How will executives like this?
Promote data control
While tools and technology are important elements of movement in general, employees also need to learn to be smart about data. You need to understand when it is useful and when not. Wrong data – or erroneous recommendations from a “smart” machine – lead to wrong decisions and waste of resources. This is where data competence, critical thinking and promoting people come into play.
Effective data collection requires practical and creative skills. Introducing intelligent analytics into business processes requires confidence in these technologies and the good judgment of employees. Even experienced data scientists may hesitate – why should they, if they have real experience, trust a machine? Less experienced users must learn to interact with and validate smart technology recommendations or integrate human knowledge to correct the course.
Can you make the change?
The change is uncomfortable, especially with the introduction of advanced technologies. But it is no longer about whether the organizations that best control their data achieve the best results. The bridge between discomfort and success will help you build confidence in these new skills.
It will be interesting to see how trust and confidence in the coming years will come from improving and developing Smart Analytics. How do people react when machines learn to extract domain knowledge from users? Do your employees know what role they play in technology to maximize the potential of your business data?