*By Erico Alexios
One of the phrases we hear most today is that data is a company's greatest asset, but the question that remains is: does your company really treat it as an asset?
I bring this definition to tease: “Asset is a basic term used to express the assets, values, credits, rights and the like that make up the patrimony of a natural or legal person and which are evaluated by the respective costs🇧🇷 Therefore, based on this definition, I need to think about the data journey, which consists of the process of collecting, centralizing (architecting), enriching, cataloging, analyzing, learning and optimizing to transform data into information that generates insights for decision making, and of course thinking about the cost of this process.
When we think about cost, it is worth considering the entire chain of the data engineering process, that is, technology versus labor versus implementation and learning time, and for that the market has been bringing a Modern Analytics Stack (MAS) approach, that is, a set of solutions with modern features for end-to-end analytics projects.
With the Modern Analytics Stack approach, you can integrate data and dramatically reduce the data engineering process, freeing your teams from the operational routine, while empowering them with insights, automation and access to advanced technology. The objective is to focus exclusively on the benefit of business users with their exclusive skills, promoting a high return on the company, removing technical barriers, seeking democratization for ALL, with simple-to-use tools, without requiring great technical knowledge, with a high degree of adoption and easy apprenticeship.
This Stack (or stack of solutions) consists of technologies that are necessary for an end-to-end analytics project, they are:
- Data integration – data movement and ingestion tools – Data Warehouse Automation, DataLake Automation, CDC (Change Data Capture), Streaming and, why not?, RPA;
- Data Storage – Solution with the Data Lakehouse concept, focusing on data sharing with governance, security and scalability;
- Data Preparation – Low Code or No Code tools, which streamline fast and efficient data processing for the business area and even machine learning;
- Analytics and DataViz platform, with self-service capability and active intelligence for business users.
There is an important point that companies are increasingly seeking to complement, which would be data cataloging, which aims to fill the gap between the business area and IT, enabling the non-technical business user to search for their data using their everyday language. day, viewing sample data, and enabling your own analysis without bogging down the IT process.
Each company is at an analytical maturity stage, but the search for a data-oriented culture is necessary, studies show that companies that do not learn to take advantage of data are doomed to be left behind, for this reason list where to start :
- Think of a cloud-based strategy, scalability and flexibility are premises when we talk about data;
- Be modular, constant evaluation of technologies and possible changes are necessary, either for reasons of technical characteristics, costs or low adoption, focus on DataOps;
- Governance is necessary, but it cannot be an impediment, always evaluate how the solutions are prepared for this challenge;
- Simplicity has to be a goal, complex solutions tend to have a shorter life cycle and adoption, in addition to high costs;
- Focus on the result, detach from technologies alone, the benefit to the business in the end is what we are looking for;
- Have partners, involve manufacturers, study bodies. Consultancies are important allies bringing guidelines and market trends, success stories and failures are important drivers;
- “Test, fail and fix fast”, resources are limited, so fix it as quickly as possible, avoiding costs by persisting in the error, for this, partners can be important allies and can support with Proofs of Concept (PoC), Proofs of Value (PoV), Minimum Viable Product (MVP);
The Modern Analytics Stack will support your company's digital transformation by transforming data into a valuable asset, fostering a culture of data-based decision-making, with a focus on revenue growth, cost reduction and compliance in the company through:
– Single platform and centralized data;
– Security and governance, without data distribution;
– Source of a single truth, with analytics and Data Science on a single layer;
– Unlimited speed and automatic scaling;
– Resilience and cost of infrastructure on demand;
– Speed and Agility allied to the business for decision making
*Érico Aleixo is Leader of the BI and Analytics Committee of the Brazilian Association of Software Companies (ABES) and SalesTech Director at A10 Brasil.
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