The benefits and positive impacts of the use of data and, above all, artificial intelligence are already a reality in the Brazilian Industry. These benefits are most evident in areas ranging from dynamic pricing in education, forecasting missed medical appointments, predicting equipment breakdowns, and even monitoring the auto parts replacement market. However, to achieve these benefits, organizations need to reach a level of analytical maturity that is adequate for every challenge they face.
In this article, we are going to discuss the concepts of AI and Analytics Strategic Planning and also look at which characteristics of the scenarios demand this type of project within the Digital Transformation journey of companies towards Industry 4.0.
What is AI and Analytics strategic planning?
The AI and Data Analytics Strategic Planning is a structuring project that combines a set of elaborate consultative activities (preferably by teams with an external view of the organization) for the survey of scenarios, mapping of analytical processes, elaboration of digital assets (systems, databases, and others) to assess the different levels of analytical maturity of teams, departments and the organization as a whole.
As a result, shared definitions of the vision, mission, values, policies, strategies, action plans, and good data governance practices are accomplished to leverage the organization’s analytical maturity level in the least possible time and cost.
Symptoms of low analytic maturity scenarios
Although there are many types of businesses, products, and services on the market, here we present emerging patterns that help to characterize the problem of companies analytical maturity and can generate interesting reflections:
- Is it currently possible to know which analytics initiatives (data analytics) have already taken place and are taking place? Who is responsible? And what were the results?
- In analytics initiatives, is it possible to know what data was used and even reproduce the same analysis?
- Does data analysis happen randomly, spontaneously, and isolated in departments?
- Is it possible to view all data assets or datasets available to generate analytics?
- Are there situations in which the same indicator appears with different values depending on the department in which the analysis is carried out?
- Are there defined analytic data dictionaries?
- What is the analytical technology stack?
- Are data analytics structuring projects being considered in strategic planning?
Other common problems
Organizational identity
Scenarios with low analytic maturity do not have data quality problems in isolation. There are usually systemic problems that involve the complexity of business processes, the level of training of teams, knowledge management processes, and finally, the choice of technologies for operating ERP, CRM, SCM and how these transactional systems are related.
Security Issues
Companies are living organisms that constantly evolve with people working in different areas. Thus, over time, control of the access levels of each employee is lost, causing unauthorized people to have access to sensitive information and also the opposite when people cannot access the data they need for their work.
Excessive use of spreadsheets and duplicates
Spreadsheets are one of the most useful and important management tools and for that reason, they are always helping in various processes. The big side effect of excessive use of spreadsheets is the maintenance of knowledge of each process. When there are two or more people and the volume of information and updates starts to grow, it becomes difficult to manage the knowledge that travels in blocks with spreadsheets. Additionally, many duplications occur and make it virtually impossible to securely consolidate data in large volumes.
What are the benefits of AI and Analytics strategic planning?
Data-driven management is expected to provide not just drawings and sketches of operations or market conditions, but a high-resolution photograph of present and future reality. Thus, it provides subsidies for corporate strategic planning in the short, medium, and long term with the following gains:
- Procedural and technological readiness for data lakes projects and Advanced Analytics and AI labs.
- Increased intensity of application of scientific techniques to businesses, such as comparative analysis, scenario simulations, identification of behavior patterns, demand forecasting, and others.
- Increased accuracy of information.
- Security of access to information at different levels.
- Acceleration of the onboarding processes (entry of new team members) who in turn learn more quickly the work scenario and also begin to communicate more efficiently.
- Greater data enrichment from increased interaction of teams from different sectors for analytical challenges.
- Increased visibility into analytics operations, Organization for localizability, accessibility, interoperability, and reuse of digital assets.
- Optimized plan of change for data-driven Corporate Governance.
- Incorporation of Analytical and AI mindset in different sectors.
- Homogenization of data policies and controls.
AI and Analytics strategic planning – Conclusions and recommendations
The preparation of strategic AI and Analytics planning is an important step to reach the level of data governance that allows the intensive use of analytics and artificial intelligence in operations since the high failure rate of analytical projects is linked to low quality of data, processes, and even the correct use of technologies (training).
Structuring projects, such as AI strategic planning and Analytics are, or at least should be, the first step in the journey of digital transformation of traditional companies. Therefore, we are convinced that in the future every successful company will have a clear and shared idea (vision, mission, and values) of what data means to them and their business model, in contrast to investments in data technology purely and simply because of the competition.
We believe that the focus on orchestrated (tidy and synchronized) data will be reflected in almost every area, for example: in the range of services, in revenue models, in key resources, processes, cost structures, in your corporate culture, in your focus on clients and networks, and in its corporate strategy.
Last but not least, it is worth pointing out that, for a successful structuring to happen, a long-term holistic approach must be taken. This means investments in optimized technology, people, and processes to enable continued business growth.
How Aquarela has been acting
Developing new technologies and new data-driven business models in a vision that the amount and availability of more data will continue to grow, taking the business to new heights of optimization.
What we do specifically for companies:
- We analyze data-generating enterprise ecosystems.
- We determine analytic maturity and derive action fields for data-driven organizations and services.
- We develop and evaluate data-based services.
- We identify and estimate the data’s potential for future business models.
- We design science-based digital transformation processes and guide their organizational integration.
For more information – Click here.
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What is Aquarela Advanced Analytics?
Aquarela Analytics is the winner of the CNI Innovation Award in Brazil and a national reference in the application of corporate Artificial Intelligence in the industry and large companies. Through the Vorteris platform and the DCM methodology, it serves important clients such as Embraer (aerospace), Scania, Mercedes-Benz, Randon Group (automotive), SolarBR Coca-Cola (food retail), Hospital das Clínicas (healthcare), NTS-Brasil (oil and gas), Auren,SPIC Brasil (energy), Telefônica Vivo (telecommunications), among others.
Stay tuned following Aquarela’s Linkedin!
Authors
Founder – Commercial Director, Msc. Business Information Technology at University of Twente – The Netherlands. Lecturer in the area of Data Science, Data governance and business development for industry 4.0. Responsible for large projects in key industry players in Brazil in the areas of Energy, Telecom, Logistics and Food.
Postgraduate in Project Management (MBA), Bachelor in Business Administration, specializing in Systems, Business Process Modeling (BPM) and Project Management.
Master in Production Engineering with a degree in Transport Engineering and Logistics. During his master’s degree, he deepened in the areas of macrologistics and regional economics, and developed research in the areas of reverse logistics, relocation of production chains, logistics outsourcing and operational research.
