A company, whatever its niche, collects an infinite amount of useful data for its proper functioning at all levels, but mainly for its business development. And, taking into account the incredible amount and complexity of information that it yields, companies urgently needed a powerful tool that could store and process it efficiently, and this is where Big Data emerged. Does this topic catch your attention? Then this article is for you. Let’s get to it.
Fundamentals of data management
Knowing how to manage the data collected is very important to optimize the reactivity, efficiency and competitiveness of the company. This is why good data management is essential for any organization.
However, this cannot be improvised, and depends especially on the application of good practice from the outset.
To get off to a good start, the first thing you need to consider is the security of your data. By failing to protect your infrastructure with the recommended software updates, or by not performing the necessary regular maintenance and postponing security reviews, you are in breach of data protection regulations, which can cost your company dearly.
Before we go on to talk about all that is involved in data management, there is one very important question we need to answer:
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What is data management?
By definition, data management is a branch of management that deals primarily with data collected by the company; especially data related to customers, market trends and competitors.
Therefore, data management refers to the business practice of creating and maintaining a framework for ingesting, storing, retrieving and saving data essential to the organization.
Data management is the backbone that connects all segments of the data lifecycle, and makes it possible to value and manage data so that they become exploitable resources for the proper development of the company.
Data management works in symbiosis with process management, ensuring that the team’s actions are fed by the cleanest and most recent data available.
Today, technology is increasingly present in everyday life, which has led to the emergence of practices based on this practice. One of the most successful examples is undoubtedly the massive use of the Internet and the birth and promotion of e-commerce.
However, many companies are not aware of the challenges of data management, which is essential if we want to ensure the proper development of the company.
How to set up data management in a company?
Data management must be applied methodically and therefore requires some technical expertise.
The approach is comparable to that seen in the management of information systems, i.e. the use of all tools, knowledge and techniques to optimize the management, security and protection of the various data collected and used by the company.
1. Make sure you have an overview:
The first step in implementing a data management strategy is to make sure you have an overview of the data you intend to manage.
The first question to ask yourself is: what data do I have?
To answer this question, you should take into account a sample of your most important data: those that have the greatest impact on the results of your activity. These will be the ones on which you should base your future strategy.
Next, consider setting priorities. Determine the most important elements of that strategy, those that will have a direct impact on each division of the company, and be sure to implement them first.
2. Develop a data lifecycle management strategy.
At this point, you must manage data throughout its life cycle, from its inception to its obsolescence. In fact, it is very important to classify data according to its age and, therefore, its relevance.
Therefore, data lifecycle management is at the heart of a good data management strategy.
3. Make sure you have the backing of the executive
A good data management strategy needs the support of the company’s executives. In fact, the most important thing is to receive the support of at least one executive who acts as a sponsor of the project.
This will ensure visibility of your program among other executives and the entire company. In addition, it will allow you to remove certain obstacles that arise in the face of the project.
4. Monitor and measure the impact of your data management strategy.
It is very important to quantify the results of your data management strategy. You should take advantage of these results to continue in the same direction or make some changes in your strategy.
Similarly, measure and monitor the results that will allow you to reassure executives and shareholders about the effectiveness of your strategy.
5. Think of data management as a product.
It can be very useful to treat data management as a product with a structured, user-centric view when preparing your project. Just like a product, your strategy should be designed for the future.
Also, as with a product, it is best to proceed gradually. Start your strategy with a small, high-impact project and then develop your strategy over time based on the results obtained.
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The importance of continuous data protection
The media keeps us up to date on hacking, as it has become the biggest risk for companies. But why is this happening? Only a few organizations make data management a priority.
The consequences of this negligence have been evident in previous years, when a number of organizations, one after another, reported failures in their security systems and sensitive data was compromised.
In a hyper-connected world, companies collect more and more data, storing it in computer databases, making cybersecurity a very important issue in the relationship of trust to be built with customers.
Without being exhaustive, we will try to make an inventory of the obligations that companies must comply with and what companies must pay attention to when they want to protect their data:
A secure password
The basic element that company employees often overlook is the password. If some are content with a misleading password (best examples: “qwerty” or “1234”), others even have the luxury of disabling the password, which is obviously dangerous for data protection.
In this case, it is up to the company to enforce password authentication. A viable solution is to establish a code; for example: uppercase/lowercase/number/symbol.
Control access to data
Access to data must be adequately controlled. Example: Establish limits to the members of the company on the access to the data online, with permissions or temporary passwords. In addition, it is also important to protect physical media (devices, paper documents, etc.).
Several studies have shown that European companies are lagging behind in this area; and they do not seem willing to submit to the numerous regulations established by the European Union.
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Advantages of data management
Data management helps organizations identify and resolve critical internal control points and deliver a better customer experience.
Importantly, data management offers companies a way to quantify the amount of data in operation.
In the backroom of any business there are a myriad of interactions (between web infrastructure, software applications, APIs, security protocols, etc.) and all of them imply possible shortcomings and/or delays in operations in the event of a problem.
In addition, data management provides managers with an overview of the business, enabling better understanding and forward planning.
Once data is well managed, you can start looking for gold mines of information, known as business intelligence. This is beneficial to organizations in many ways. Some examples may include:
Intelligent advertising that adapts to customers based on their interests and interactions
Global security that protects crucial information
Alignment with the relevant compliance standards, saving time and money
Machine learning that, over time, becomes more aware of its environment and fosters automatic continuous improvement
Reduces operating costs, using only the computing and storage capacity strictly necessary for optimal performance
Both consumers and buyers benefit from good data management. By knowing their preferences and buying habits, companies can provide their customers with faster access to the information they want.
Customers can enjoy personalized shopping experiences and be assured that their personal and payment information is securely stored, simplifying their shopping experience.
Data management challenges
All these advantages do not come without overcoming some obstacles. In fact, as information technologies are evolving rapidly and increasingly, data managers will undoubtedly encounter many obstacles along the way.
There are four main challenges in data management that we would do well to anticipate:
The amount of data is generally too large
Many organizations share data. The development team may work from one set of data, the sales team from another, the operations team from another, and so on.
The path between structured and unstructured data is a difficult one. Many times data arrives randomly to a company. Before using it to manage business intelligence, it is necessary to prepare the data: it must be organized, free of duplicates and “cleaned” in every way.
It is essential to have a good data management culture. All the processes and systems in the world are useless if people don’t know how to use them. By making our team members aware of the benefits of management, managers motivate them and make them pillars of the information process.
Fortunately, there is already a countermeasure against a couple of the challenges mentioned here, and that is Big data.
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What is Big Data?
An incredibly huge and constantly growing amount of data, often complex, that is almost impossible to process with traditional methods.
Although storing and accessing large amounts of information for analysis is not new, the term “Big Data” became popular in the early 2000s.
You might be thinking: okay, so Big Data is a large amount of data, why the fuss? So, to give you an idea, we’ll tell you something: 1000MB equals 1GB, 1000GB equals 1TB, 1000TB equals 1 Zettabyte, and Big Data equals several zettabytes and is constantly growing.
Can you imagine the amount of information contained in several thousand terabytes? Now, imagine processing all that data with traditional methods. It seems like a never-ending nightmare, doesn’t it? And here’s why Big Data is currently the data tool par excellence.
This concept is linked to several characteristics known as “the V’s”, and ranges from three V’s to six V’s. These are:
We could say that this is the main characteristic. In fact, for data to qualify as Big Data the volume must be enormous.
This characteristic refers to the type and nature of the data. There are three different types:
These are different types of quantitative, clearly organized and defined data that are easy to search.
This type of data does not have a fixed structure. Even if it does not obey a rigid organizational scheme, it has some labels or markers that provide some kind of order.
As its name suggests, this type of data is unstructured and entirely qualitative. Its sources are varied, such as texts, conversations in social networks, posts, videos, images, etc.
Refers to the speed at which data is generated and processed to meet demands.
Reliability of the data. This aspect also takes into account the quality and value of the data.
This refers to different aspects, such as the cost-effectiveness of the data analyzed, the value of the information collected and the valuation of all other Big Data features.
Variation and combination of formats, structures and data sources. Structured, semi-structured, unstructured and raw data integration are combined.
With planning, practice, the right partners and technologies like machine learning, you can turn pain points into opportunities for more business insights and a better customer experience.
When creating a company, it is essential to control your database and protect it; therefore, data management must be at the core of a company’s strategy.
At TAS Consulting, we will take care of analyzing your data (tax form, customer files, quarterly balance sheet, financial data and social data); in order to obtain them periodically, and thus, establish a compatible data management strategy.
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