Data lake: 5 best practices

When a company is confronted with a large volume of data, a data lake quickly becomes essential. A veritable reservoir of raw data, the data lake represents a powerful tool if it is thought out correctly. In this article, we give you the five best practices to respect so that your data lake does not turn into a data swamp as the insiders say.

 

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What is a data lake?

Creating a data lake means centralizing raw, unstructured data at the enterprise level. All staff or interested parties can then navigate through the data lake to access and manipulate the data. The data lake therefore allows the data to be stored for easy exploitation at a later stage

Making the data lake navigable

The data lake contains raw, unstructured data from different sources. This does not mean that the data is stored in it jumbled together without any logic.

Data lake users must be able to locate the data they need. Indeed, the data lake is intended to provide a centralized access point for data. It is therefore important for everyone to be able to find their way around.

For this, metadata is essential, and an adequate methodology must be developed to add data to the data lake. The company must provide its employees with a compass to find their way.

Ensure that the data lake is accessible

A good data lake is not only for experienced sailors! The various employees who will use and manipulate the data are not necessarily experts in the field. It is therefore important to simplify their lives in order to encourage the adoption of the data lake and the transformation towards a data-driven corporate culture.

The various users must therefore be able to locate the data they need, cleanse (or structure) it, standardize it or consolidate it. This implies a real work of communication and training upstream. Some software such as the one developed by Ryax also facilitates this harmonization by offering a unified framework at the enterprise level.

In the future, any employee will have to master some basic tools for data analysis. This can be compared to the arrival of the computer in the company. Thirty years ago, only one in four employees used a computer. Today, this rate is close to 100%. In spite of this evolution, the human being is still at the heart of many organizations and spending time and money to help employees adapt will therefore pay off in the long term.

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Defining goof fata governance

Let us face it, few organizations are willing to devote resources to implementing sophisticated data governance.

Yet data governance is still too often overlooked, but it is essential. Policies regarding data use must be clearly established and communicated at the enterprise level. Good data governance helps to avoid errors and to unite the different actors of the company around a common will.

In particular, the Chief Data Officer or data manager should consider the following:

  • Define different levels of access depending on the category of users. Some data is more sensitive than others, and not all departments or employees need to have access to all data;
  • Establish a clear policy for adding data to the data lake (e.g. metadata as discussed above);
  • Ensure that there is some form of control over the use of the data.

Guarantee the security of the data lake

Data security is an issue that is too often underestimated. Although investments are increasing, so are the threats. The proportion of indirect attacks has risen sharply in recent years, as Accenture notes in its 2020 report: "Innovate for Cyber Resilience".

Beyond malicious attacks, a careless employee or a computer bug can alter certain elements. Making decisions based on poor quality data: it doesn't work.

In addition, it is important to ensure compliance with the rules in force, particularly regarding the protection of personal data (for the European Union, see our article "Data and GDPR: what you need to know".

Think scalability

From its conception, a data lake must be scalable. Indeed, the amount of data available is growing exponentially, due in particular to the development of the Internet of Things. According to some sources, the number of connected objects should exceed 40 billion by 2027. It has been a long time since the human mind has been able to grasp the volume of data created. In order to ensure the scalability of the data lake, some questions need to be asked from the outset:

  • Should a cloud, local or hybrid structure be preferred?
  • Will I manage my data internally, or am I ready to outsource some aspects of data processing?
  • Should I opt for vertical or horizontal scalability?
  • What are the costs associated with the ongoing deployment of the system to ensure this scalability?
  • What are the limits of my data lake?
  • When should I clean up my data lake?

The need for a global approach

In 2020, collecting data is as easy as 1-2-3. However, using data cannot be improvised. When a certain volume of data is processed, a data lake quickly becomes indispensable to maintain a semblance of organization.

Ryax offers you an unlocked (open source) platform to process all your data flows. Thanks to its intuitive interface, to apprehend the Ryax software and to become familiar with an analytical data processing becomes within the reach of the greatest number.

The use of a single data science platform greatly simplifies the implementation of a good data governance and a data-driven corporate culture. To learn more about this unified and harmonized solution at the enterprise level, contact our teams today.

The Ryax Team.