Understand in which stage of the journey your company is at. Thus, it is possible to apply solutions that match the reality of your business and the results are more positive.
Data analysis is a journey, not a destination. To do a good analysis of your data and interpret it in the best possible way, you need to know what stage of the journey you are in to evolve as your business evolves.
There is no point in applying a sophisticated solution if the timing is not adequate for it. The implementation of Machine Learning or Artificial Intelligence, for example, will not bring the expected and promised results without a previously traveled path. That is, without a well-defined data architecture and data-based management.
But don’t worry, our team of Business Intelligence consultants, Data Scientists and Engineers, Big Data Architects and Cloud Architects will guide your organization in its analytical evolution, so that you can effectively achieve positive results and create new opportunities for your company.
Data Analytics Journey steps
The data journey is divided into a few steps. Below, we explain what are the 4 main phases to be covered.
1. Descriptive Analysis
This is the first stage of the Data Journey, the most performed analysis and the best understood among all stages. It characterizes and classifies the data, including dashboards, reports and types of database queries, to analyze and understand the company’s performance. This type of analysis helps in understanding past events as well as events happening in real time, and should answer the following questions:
• What happened? What is happening now? How does this relate to the expected plan?
• How much? When? Where?
2. Diagnostic Analysis
The second stage of the Day is the diagnostic phase, where we will understand why it happened. For this, unlike descriptive analysis, here it is usually done the analysis (or correlation) of more than one data source, combining information from different sources to obtain an answer.
• What exactly is the problem? Why is it happening?
These two initial phases of the Day do not have robust techniques that facilitate the understanding of what may happen in the future, nor do they provide suggestions for what to do next. However, through Business Intelligence, they guarantee insights into what is happening at the present time and what has happened in the past, but it can be useful for decisions about the future.
In addition to ensuring visions of performance improvement and process optimization, these two phases are the first steps towards a well-done and efficient application of predictive and prescriptive analysis.
3. Predictive Analysis
From that point forward the term “advanced analytics” can be used properly. Now that we have an understanding of the past, we can make “predictions” of the future. That is, based on the history and learning of what has already happened, it is possible to program a machine to identify patterns and relationships between the data. Then, just extrapolate these relationships to the future, predicting what is yet to come. This is the famous Machine Learning.
• Based on what we know, what will happen next?
• What data are correlated with each other?
• What factors most influence a given outcome?
• What will happen next if?
Data is the essence of predictive analysis and, in order to have a complete view, several types of data are combined: descriptive data (attributes, characteristics, geo / demographics), procedure data (orders, transactions, payment history), data interactions (e-mail, chat transcripts, click flows on the web) and behavioral data (opinions, preferences, wants and needs).
4. Prescriptive Analysis
Finally, the prescriptive phase: now that we understand the past and can make predictions about what may happen in the future, it is time to think about what the best action or response will be.
This is achieved through techniques that computationally determine a set of alternative actions of great value. Based on the company’s objectives, requirements and restrictions, it is important to always aim to improve business performance.
These techniques are applied to input different sets of data, including historical and transactional data, real-time data feeds and big data.
This is the phase in which the most data is worked in real time, since to prescribe it is necessary to know what happened at the exact moment. This is information latency, a very important aspect to have a prescription at the time of action.
Many companies, not to say most, are concerned with optimizing just one or two of these steps. Whether for lack of resources, lack of interest or simply lack of knowledge.
But one fact is: to reach a level of digital transformation that will differentiate you from competitors, provide competitive advantages to stay on top or boost your trajectory there, you need to carry out strategic planning that integrates all these stages. Thus, it is possible to evolve from insights generated by BI to the use of advanced analytics, such as AI and ML.
We are here for that. Let’s go through this Journey together!