Technology is useful today in many areas, including data, through which we can handle this large amount of information. Something that without her would be totally impossible. All companies are aware of the importance of this data in order to be more accurate and efficient businesses, and therefore, they are also aware of how critical the choice of the right technology is to achieve success. In this post I tell you how to choose those data tools, through 5 steps. Go for it?
The increase in the use of artificial intelligence (AI) is bringing a new wave of data to companies, in extremely large volumes. All this data is very useful for business, but many companies do not know how to interpret or analyze such large amounts of information.
Effective data use and management, as well as analytics, are critical to keeping businesses humming through 2025, according to a report by NTT Data and Oxford Economics . Most of the 500 executives surveyed agreed that data was necessary for an organization’s financial performance, growth, customer experience, employee experience and overall industry competitiveness, according to the report.
One of the biggest challenges in data analysis, however, is figuring out which analytical tools to use . And it is that, as new analytical tools are launched, companies have more difficulties to decide which is the best option for them. Also, keep in mind that it is very important that all teams in a company use the same data tools. Another fundamental factor is the supervision of these tools. Without any oversight or standardization in analytics tools, companies would fall apart and the data would go unused. Therefore, it introduces the supervision of the use of data tools, once implemented. Something that very few companies put into practice today.
Many times we make the mistake of focusing on the raw material, that is, the data. We look for data sources, places to store them, manage them… but we lose sight of the technology that can transform that raw material into something of value. In this case, transform data and information into knowledge for people.
It is in this space where technology becomes important, being a means to facilitate the analytical experts, their process until turning that information into a true asset of business value for the company.
But the difficulty also comes when deciding the most suitable tool for specific needs, an exact industry or a company with “x” specifications. That is why it is so important to create a correct methodology that will help you successfully select the most appropriate tool.
These steps that I present to you are not my invention, nor Inquisitive Technology ‘s invention . They are a methodology developed by two American experts named Levy and Wells , who met with a large number of business leaders, who were asked about their business needs. After that, it took them a while to create a process that executives could understand, with empirical data to support these decisions. They both wanted to create a methodology that could be used in several different situations, with different organizations, for different purposes. And this came up.
The 5 steps to choosing the right data analysis tools from Levy and Wells.
Levy and Wells put in many hours, interviews, and prototypes until they came up with these five steps. Fundamental to be able to successfully decide on such a critical element for a company as is the data analysis tool that will provide data scientists with knowledge when drawing conclusions. The 5 steps are these:
#1. research and discovery
First of all, the current status of analytics tool implementation and analytics capabilities within the company should be determined . To do so, in-depth interviews must be conducted with key stakeholders, including business intelligence developers , administrators, and IT executives. Essentially, you need to interview the people who will use and benefit from the analytics tools.
These interviews help to understand the details of who uses those tools, what they are using, which ones they use at that moment to carry out their work, what these data tools do not allow them to do and if they are being used correctly. Are these tools being used to the best of their abilities? Do they have the inside knowledge to get the most out of your software portfolio?
2. Overview of the current state
The second step involves taking an inventory of the current analytical tools on the market and separating them into different classes. These classes of tools include report writers, semantic layer reporting tools, MDX/Cube query tools, data discovery and visualization tools, integrated BI and reporting tools, data science and modeling tools, as well as data science and modeling tools. in machine learning and artificial intelligence use cases.
Where is the next wave headed? What is the landscape like in terms of the various providers and the data tools they offer? And according to the needs that you found in the first step, you detect which tool could work.
3. Capability tree
The third step uses a capability tree to compare the results of step one and step two, looking at the company’s current inventory rankings against the overall market inventory.
The capability tree is useful because companies can see the areas in which they are doing well or lacking , based on the data tools that are important in the marketplace.
4. Decision matrix
The decision matrix is where for each of these classes or sets of tools, or if you’re making a specific vendor selection, for each of these vendors, the various capabilities are entered and qualified. The score will be based on the needs of the company, giving more weight to the most important capabilities for the business.
For example, data science. It is known from experience that a data science tool is really good for creating advanced algorithms, but maybe not so good for displaying dashboards. The experience of each one can be used to qualify the different classes according to the abilities that were defined.
5. Decision tool
Finally, the company uses a decision tool to match the best tool with each business capability.
A decision tool is a combination of the capability tree and the decision matrix , in the sense that each of the capabilities is weighed according to what is most important to the company or to any particular project being undertaken. . What you need to do is weigh the various capabilities, and the decision tool should return the weighted score of all these capabilities, telling you which one is the right tool.
Regardless of these steps, company leaders should spend a lot of time studying their own company and figuring out where help is most needed . None of the data tools will be useful if none of them solve the real gaps and problems within the organization.
