Companies and organizations are increasingly aware of the importance of correctly managing and using the enormous volumes of data generated daily, to better understand their users and offer them what they really need. To consolidate and correctly take advantage of this gigantic information flow, the market today offers these two valuable and complementary tools.
The need to adopt agile and efficient management strategies that make it possible to meet the multiple needs of an increasingly segmented and specific market, poses various challenges to modern companies and organizations.
The most important of them lies in knowing in detail each of these particular requirements, in such a way as to have strategies that ensure timely and efficient responses.
In the opinion of experts, the best way to face this scenario is to collect a large amount of data from the target market, both general and specific. However, this also means managing this data in real time and analyzing it accurately. Only in this way, the resulting actions will be reflected in the fulfillment of the strategic objectives of each company.
To carry out this task, technology and digital evolution provide two precise and highly efficient tools: Data Warehouse and Data Lake.
The concept of Data Warehouse used for the first time by the American computer scientist Bill Inmon, means literally “Data Storage Physical Space”. However, its usefulness goes far beyond that functional simplicity.
In the first place, its main objective is to facilitate data processing, in order to analyze information from different points of view and at high speed.
For this, it is essential to perform multidimensional analysis. This will allow us, for example, to know the number of sales of an “X” model car, in royal blue color, at the Bilbao street branch in Santiago, between 2018 and 2020.
Although, apparently, it is a complex process, due to the large number of variables mentioned, a good Data Warehouse makes it much easier, since it manages to hierarchize previously all the information, from the creation of different dimensions.
This way of organizing information allows to structure it logically and ultimately, in order to provide valuable data facts to the work of the data analyst.
According to Bill Inmon’s definition, this tool consists of the following features:
1) The stored data must be integrated into a consistent structure. Likewise, the information is structured at different levels, adapting to the needs of each of its users.
2) The data is organized by subject, to facilitate its access and understanding by users. For example, all sales data should be stored in the same place, so it would be easier and faster to make a relevant query.
3) Although the data represents a present moment, a Data Warehouse stores all the different values of the same variable. This allows a better analysis of trends, as well as the historical evolution of the issues.
4) All information stored is permanent and should not be modified. In the same way, every time new values are incorporated, no action should be taken on the existing ones. This will provide better conclusions to the respective analysis processes.
In addition to these four basic characteristics, every Data Warehouse must have a good metadata organization. In other words, it requires appropriate tools to logically classify the series of data on which neither its origin, or its reliability or way of calculating it is known.
This will optimize the analysis work, starting with the construction of more accurate and relevant queries, reports or analyzes.
The implementation of Data Warehouse in the data management of a company translates into the following direct benefits:
– Facilitates decision-making based on data, in any functional area of the company, since it provides integrated and global information on the entire business.
– Transform information into added value for any business, thanks to the application of statistical analysis and modeling techniques, which help to find hidden relationships between stored data.
– It allows, in a simple way, to learn from the data of the past and to predict future situations for different scenarios.
– Simplifies the implementation of comprehensive customer relationship management systems within the company.
– Optimizes, technologically and economically, the environments of Information Centers, statistics or report generation. This translates, at the same time, into great investment returns.
– It is especially useful for strategic work in the medium and long term.
– Substantially increases the productivity of companies.
– Allows planning much more effectively.
– Integrates in a single solution all the corporate tools and applications used to collect information, such as web monitoring, CRM and Wi-Fi tracking, among other options.
DATA LAKE’S CONTRIBUTION
Another concept that is currently emerging within the new collection and analysis strategies is “Data Lake”. Basically, it is a large warehouse of raw data, which remains unchanged, from arrival to use. Unlike Data Warehouse, which works from hierarchies and differentiation by files and folders, a Data Lake has a flat architecture.
We could say that Data Lake is nourished in real time by Big Data and information, both structured and unstructured. This forms a flat amalgam, from which we collect and analyze only what we need in a particular time.
Its main characteristics are the following:
It is associated with Big Data, in the sense that it is the container where all those data obtained rest. As they are not organized, we need an efficient search for information. Basically through machine learning technology.
Effectively analyze the degree of protection of the information that is stored in the different sites.
It allows to be fast and to have data in real time. In addition, it helps us to quickly prepare and share critical information to deliver competitive analytics.
Helps save data preparation steps for rapid replication within automated processes. Furthermore, with an intelligent Data Lake, these processes can be accessed without pauses or intermediate stops, reducing time and hours of work.
DATA LAKE BENEFITS
The most prominent advantages of a good Data Lake are the following:
– Centralize all data in the same place, wherever they come from, so that they can be processed through Big Data tools with a high level of security.
– Allows access to the original source of valuable data for analysis, even if it is outdated or disabled.
– Normalize and enrich all data that is stored.
– Prepares the information according to the need of the moment, considerably reducing costs and analysis times.
– Allows any authorized user to access the information, and enrich it from anywhere on the planet. This helps companies more easily collect the data needed for a decision making.
– Puts information in the hands of a greater number of people within any organization, which makes the most of the knowledge acquired by those individuals.
DIFFERENCES BETWEEN DATA WAREHOUSE AND DATA LAKE
Although Data Warehouse and Data Lake are, in essence, “sister technologies”, there are specific differences between both tools, which can be summarized in the following points:
A Data Lake holds all the data, not only what might be used now, but also what might be needed in the future. On the other hand, the Data Warehouse studies very well what data to include and what its sources are. This results in the use of very different hardware in each case. In Data Lake, the expansion to terabytes and petabytes is much cheaper than for the Data Warehouse. For this reason, in the latter case, it is well analyzed which data will be kept or deleted, since it involves a higher cost of storage.
A Data Lake supports all types of data, regardless of its source and structure, and maintains it in its raw form, transforming it only when it is going to be used. In the Data Warehouse, on the other hand, the stored data is much more critical for business and reporting. For this reason, images, comments on social networks or less relevant texts are sometimes deleted, for example, since their storage is very expensive.
Data Lake are more flexible than Data Warehouse. Therefore, adapting a Data Warehouse involves investing a lot of time in the development of the warehouse structure, which is not always positive for companies and organizations that must give quick answers to their commercial questions. On the other hand, the Data Lake, by storing all the raw data, allows access by any user, so that they can exploit and analyze it according to their needs, finding a way to answer these questions at a more agile pace.
Data Warehouse provides cleaner, more structured, accurate and reliable results. This allows it to target more Data Scientists who create their own rules and structure the information in order to prepare analyzes and models. On the other hand, Data Lake provides less precise, but faster answers, ideally designed for less qualified users, People who simply seek to have access to certain daily KPIs.
Beyond these specific differences, both Data Warehouse and Data Lake can coexist without inconvenience in companies that base their decisions on data. In other words, they are complementary and not substitutes, since both have the ability to help any business to better understand both the market and consumers.
According to this, both systems are excellent alternatives to design and implement strategies based on in-depth knowledge of the target audience.
All of which will translate into increasingly personalized and segmentation-oriented communications, factors that are key to the new strategic organizational success.