Insights 14 May 2021

Turning Data into Valuable Insights

All businesses are already aware that in the digital era “data is a new oil”. Data become the main driver of economic growth. The more data are available and are used, the more business opportunities and competitiveness there are. Today we will learn about the foundations for the use of data, which consist of data mindsets and the efficient use of advanced data analytics and big data, with Mr. Phiphat Prapapanpong, the Director of Data Science (Machine Learning) of Bluebik Group Public Company Limited.

There are 5 steps to make the most use of data.

1. Data analytics and use case generation

Firstly, we must develop a mindset so that all departments will understand the importance of data and their efficient use for business gains. For example, findings from data analytics can be used to increase the revenue of businesses, cut operating costs and improve sales methods to raise sales.

To see how an organization can use its data, we must understand the operations of all its departments, its products, its services and its communications with customers and study the data that are generated in each business process. Then we will analyze the data and develop them into data analytics use cases to find opportunities and gaps for development to reach business goals. With the use of data, organizations can see the paths of their development clearly and can use data to efficiently support their business strategies.

2. Gap analysis

After business organizations see and understand the goals of their data analytics, they will find the gaps that get in the way of their system development. They may have to look at their foundations which are data infrastructures so see if the foundations are ready to support data analytics. For example, a common problem of most organizations concerns data quality. Data may be stored in the formats that obstruct analyses. After problems are analyzed, the discovered gaps must be considered to develop the action plans that will truly enable organizations to make the maximum use of data and develop use cases as planned.

3. Data foundations

After we are ready in terms of data and the use cases that meet business goals, we must have a unit that will prepare data use from the stage of planning to the action plans that will efficiently support the development of use cases and data analytics. Data foundations comprise these three main parts.

  • People – They must be readied. This begins with a structure of data supervision which includes data administrators, data service teams and stakeholders. The readiness ensures the efficient and reliable management of data.
  • Process – This concerns the initiation of a data management process which covers the planning of data use, data storage, backup data, data restoration, archives, data migration and data destruction. The process ensures that data will be always ready for use and accurate.
  • Technology – Organizations must have their data platform or data lake to store central data, reduce duplications and disorganized data and create compatibility with various instruments for data analytics such as the data warehouses, which support the compilation of understandable reports (visualized dashboards), machine learning and real-time data analytics.

4. Roadmap

After organizations are ready in terms of strategies, business goals and the three foundations, they must work out action plans to efficiently achieve business goals. Tasks must be prioritized. Normally a priority matrix is applied with two axes of impacts and readiness and their high, medium and low levels. When tasks are put on such matrixes, business organizations can conveniently and efficiently draw their action roadmaps which cover short and long-term tasks.

5. Data science implementation

The ultimate goal is to translate data into real business opportunities. To find patterns and achieve business goals, data are processed with analytic models which include algorithms such as classification, regression and clustering according to business purposes set in the first step. Outcomes from data analytics must be useful for business operations. For example, outcomes from product recommendation engines can be fed into a marketing system which will present better personalized promotions to prospective customers.

The future use of data will reveal the behavior of things

Many business organizations have realized that data are precious assets and can be used to create opportunities and prevent mistakes. Large-scale organizations worldwide begin to adapt and find new business opportunities by using “the data that display the behaviors of consumers”. Obviously electronic devices are collecting consumers’ data. They include highly developed wearable devices like smartwatches and smart glasses. There are also the applications with which consumers willingly share the information of their everyday life. They include mobile banking applications and the applications that require facial recognition before transactions. Besides, IoT devices are providing organizations and businesses with new kinds of data about consumers. The organizations that are planning to use data or installing data application systems should update their plans and systems to follow trends.

Product development may target not only customer experience but also total experience

Another significance of data analytics, apart from customer experience, is that it supports the creation of good experience for all parties, from staff to customers (total experience). This greatly strengthens business because business concerns not only consumers. The creation of positive experience from within will result in the valuable experience of end users. This can lead to the sustainable growth of revenue and profit.

The attractiveness of data consulting

Apart from learning new technologies and interesting behaviors, data consultants also gain experience in business. This is because the good data analytics that will make business grow efficiently should begin with business goals. This work suits the people who are enthusiastic about technologies and good at noticing and have creativity in the analyses of new formats of data. These qualifications mean considerable work of quality.

What skills and mindsets does data consulting require?

Work with data and analyses may require basic knowledge about the use of instruments and data analyses (technical skills), ability to interpret data and see the causes and effects of behaviors (logical thinking), the  business understanding which leads to the understanding of data and credible presentations, the mindsets that help build strong teams (leadership), the open-mindedness which is necessary for collaboration with experts from various fields and the empathy that supports understanding, concerted efforts to solve problems, happiness at work and progress.