What are the most common mistakes organizations make when choosing data analysis tools?

Not understanding the requirements is probably the biggest mistake in selecting a tool. You need to understand what users need the tool to do. You need to understand what they do now and look for opportunities to do things better in the future, as long as the users are on board with the changes. You don't just pick the tool you've heard about at a conference, the tool a paid analyst recommends, a tool a friend uses, or a tool that comes with vendor perks when it isn't going to work with the existing user base.

Focusing on the current hot topic need as the reason to select the tool is one of the top reasons data management projects fail. Map out how the data moves through the organization and what the i/o is needed to support the functions. Then and only then decide how and where to collect the data to analyze against. Otherwise you are analyzing data that may not even be in the right state, unfortunate for many making decisions off of incomplete or incorrect data.

One Size fits everything approach during the selection process of an enterprise tool is a major mistake. The major areas one needs to consider during the selection are defining the why aka Purpose of the tool and how it helps, along with those functional requirements one needs to understand the current data landscape and evolving needs of the enterprise to align and make informed decisions around Integrations, Security, Privacy, current knowledge etc. Rolling out a self-service BI tool by itself without having the support structures in place will create friction between stakeholders and put organization at risk.

Scalability especially for enterprise level is a major challenge that many companies don’t realize until they have already made the purchase and have tried to use the tool. Tools may only connect to a limited number of data sources which may limit the analysis. It may also fail to read through the petabytes of records and stop connecting after a certain point. More importantly, the architecture of the solution can be archaic that it no longer is compatible with other technologies in your organization’s tech stack. These issues may be causing more time and money to fix rather than spending the effort to rearchitect.

Having a tool that is both scalable and compatible is a long-term investment that will have continual upkeep. Choosing and implementing the correct tool is a long and arduous process that will cost a lot of time and money within organizations that have pieced their data systems together over time. Ideally, subject matter experts on every single data stream and every data user would be included in the evaluation process. I'm not saying this is realistic, just be sure not to overlook potential complexities because you didn't invite the right people to the table. When making a choice, remember that data quality issues and mishandling of information could always end up being more expensive, so really think about it before going the cheap route.

Data analysis tools can be just that - a tool to use to get a job done. But with the right levels of compatibility, training, processes and the right people in place to scale it, tools can be infinity more powerful than 'just a tool'. The data landscape & ways of working need to be assessed before deciding on the right tool for the right people in the right place.

An Analysis tool is supposed to be easy to use. If it is not, then your end users just won't use it. Instead, they will probably use your tool to export the data they need, and then resort to doing their analysis in another tool like Excel. An Analytics tool will only succeed if it's usable.

Cumbersome data analysis tools lead to very specific people in the organization can actually work with them. Usually these people are real gems that get things done. And usually, when these people leave to other places or other responsibilities, everything gets stuck, and people are left with old information analysis that they already have, without real ability to move forward.

Sometimes you do not need a fancy tool, but rather focusing on a tool that can allow you to easily navigate among many systems. For example, you can use Python to communicate with API, serve as a server, carry out geoprocessing tasks, create Language Models, and work with various databases: from the most basic MSSQL, Oracle to Teradata, or Hadoop, I have done all these seamlessly using Python.

Even the most advanced data analysis tools are ineffective without a well-trained team. Organizations sometimes underestimate the importance of providing adequate training for their staff. Without proper training, team members may struggle to use the tools effectively, resulting in suboptimal analysis outcomes. Investing in training ensures that the organization can leverage the full potential of the chosen tools. Many organizations already have existing systems, databases, and software solutions in place. Failing to consider how the chosen data analysis tools will integrate with these existing systems can lead to data silos and inefficiencies.

In my experience, decision making should involve analysts and developers, not just leadership team. -Product showcases are normally scratching the surface, not addressing the real pain points team is experiencing. -Dedicated resources need to be allocated to evaluate the new tools. -This means extended trial licenses and a POC project to fully understand the functionalities and performance

Um dos erros é escolher a ferramenta de forma completamente agnóstica aos ambientes que a própria empresa já usa ou que pensa usar no futuro. É, pois, importante estar consciente se as conexões dos sistemas legacy - por exemplo - à nova ferramenta de BI funcionam, porque se a integração entre eles for algo difícil ou que gera entropia ou até mesmo que seja incompatível, isto pode comprometer completamente a escolha feita.

In addition, hidden costs of a data analysis tool include the training requirements for full adoption of the product as well as the technology resources to support the platform. These costs can often supersede the initial cost of the software purchase however without a proper budget allocated to these hidden costs, it will appear to be a failure if the users are not trained and prepared to use the analysis tool.

When it comes to comparing and evaluating the costs and benefits of management tools, especially in the context of data analysis and business intelligence, there are several techniques and frameworks that can be employed, but often overpassed by managers. That's one of the added value of management consulting. Indeed they will use Cost-Benefit Analysis (CBA), Return on Investment (ROI) Analysis. Total Cost of Ownership (TCO). Since TCO encompasses all costs (purchase, operational, maintenance) over a tool's lifecycle it's may be the best suited in BI tools. For example big software can have huge economy of scale compared to small ones (more forums, more support, better SLA etc)

Companies sometimes pick sophisticated tools without understanding their needs in alignment with their vision, like getting an espresso maker when they just need to boil water. Also, if they don’t engage end users since inception of project as well as do not show employees how to use these tools, they might not be adopted well, like giving out smartphones without instructions.

I would recommend that the evaluation process should really be 360° and do not consider only 1 or 2 factors over the rest. There is a lot of complexity navigating the landscape of Data Analytics tools and sometimes we just try to pick the one with the best commercial pitch, especially "easy to use" and "beautiful charts". The reality is much more complex. The decision maker should have clear evaluation criteria (including technical ones, like the integration capabilities or the scalability, or the data security governance) and do a complete due diligence and not jump to select the first tool they are presented with just because it looks fancy or because they are offered a free POC.

Here are the key mistakes organizations make when choosing data analysis tools: 1. Not defining clear requirements. 2. Ignoring scalability considerations. 3. Overlooking data integration capabilities. 4. Neglecting user skill levels. 5. Focusing solely on cost. 6. Falling into vendor lock-in. 7. Ignoring data security and compliance. 8. Not considering future trends. 9. Lack of user input in the selection process. 10. Insufficient testing before implementation. Avoiding these mistakes involves thorough research, stakeholder involvement, and alignment with specific needs and goals.

In addition: not being critical about professed requirements. Organisations tend to overinvest for functionality that in the end is hardly used