5 Common Mistakes That Can Cause Your Analytics and Big Data Project to Fail

The impact that an Analytics team brings to a company's business is evident. Its greatest contribution lies in the ability to deliver valuable information for each business area of an organization, presenting indicators that support decision-making.

More and more companies are looking to build their Analytics teams. For this reason, we ask ourselves:

How simple is it to build an Analytics team?

If a company already has a team, are they really achieving the best results?

Are there still aspects that can be improved?

Statistics show that more than half of Analytics projects will never reach production environments or will be discontinued due to not delivering the necessary indicators.

And a large part of the projects will have a higher cost than appropriate, by not using much of the captured data.

At Kranio we are data specialists and we have gathered the top 5 cardinal sins that an Analytics and Big Data team may be committing and that you should avoid. Keep reading and find out if you have fallen into them.

#1 - You are doing the project without involving the business user

For an Analytics project, a data source with terabytes of information, real-time data, state-of-the-art services, or graphical interfaces with the latest UI/UX techniques on the market is not enough.

If your project does not deliver the necessary indicators to answer the questions of the business user and the organization, your project will fail.

One of the most important points of the Analytics team is to be able to answer key questions of the company:

What campaign can I run, when and where?

What audience can I serve?

What do my customers do and prefer?

Which products and services should I improve?

What is the impact of my decisions?

The information and indicators presented by an Analytics project must impact a company's results and also decision-making. Data has no value if it does not trigger a business action. 

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That is why it is important to know the purpose of each data to be worked on, and nothing better than having the participation of the business user.

This way you will have an Analytics team with technical expertise and knowledge of your business.

#2 - Your team is formed for an IT project

Often, organizations build Analytics teams similarly to IT development teams.

IT development projects, such as web applications, focus on providing new functionalities, improving user interactions, and having more predictable build and deployment times.

But an organization must always be attentive to changes in the environment it lives in and constantly seek to improve to continue delivering results. And as we told you in sin #1, it is important that an Analytics team is as close as possible to the business.

Therefore, members of the Analytics team should:

• Be more attentive and receptive to changes, rather than imposing development times.
• Control data quality before exposing it on a dashboard.
• Be data specialists rather than development generalists. If you don't have them, you can hire them quickly and remotely for a project.

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You need to understand which tools and work methodologies will get you to results faster. In our experience, we can recommend:

• Apply time blocks that respect the business instead of very tight or very long times.
• Focus on the Value of deliveries over Man-Hours
• Separate and maintain small groups with limited context by business area instead of global Analytics teams.
  

#3 - You don't have a data culture

If you do, perhaps you are not considering operational continuity.

As you saw in the previous points, there are organizations that build their analytics teams similarly to an IT development project.

In a data culture, people have a shared purpose: to use data to make better decisions. Together, as an organization, they understand the data and amplify the impact it can have on different business areas.

It is common in this type of projects that your team uses manual processes for data extraction, transformation, cleaning, service deployment, or others. You also lack visibility of the data cleaning and transformation flows for consumption, not covering failure cases or data loss along the way. This makes scaling a nightmare.

It is very important to have predefined procedures and solutions from the start to ensure operational continuity. You should ALWAYS consider at least:

• Not using, or minimizing, manual validations.
• Providing visibility of the data transformation and cleaning flow for consumption
• Raising and using metrics to guarantee data quality at each applied transformation.

In modern organizations, having a data culture is not enough; it is essential to operationalize data in every Analytics project.

#4 - You don't have a clear definition of your stack

One of the most fascinating things about working with Analytics projects is the diversity of options for tools, services, and technologies. It's a huge universe!

It is common for these types of teams to be constantly using new tools or creating new solutions in different ways.

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However, the risk is ending up using a large number of services and technologies:

• More complex maintenance.
• Obsolescence of solutions requiring new developments.
• Need for more team knowledge.
  

Having engineers and architects to define and update the project's stack—the set of tools and technologies—will help limit complexity and simplify workflows.

With a well-defined stack, incorporating new team members is easier, the requirements to apply for the position are more specific, and the operation of services in production environments is simpler.

There is also the case of teams with defined stacks that have not been updated. It is very important that your team is open to improving the architecture, incorporating the use of new tools available on the market, or integrating new concepts, because it will help the business achieve results faster.

#5 - You depend on on-premise infrastructure

One of the main issues when working with analytics projects is Scalability.

As your project progresses, you will have more data, users, and indicators. It is essential to be able to scale in capacity, speed, and at a reasonable cost. However, increasing on-premise infrastructure is slow, expensive, and complex.

The analytics team cannot be tied to local infrastructure. Although you will have legacy and on-premise systems that generate data, once injected into the analytics project, all processing is in the cloud. This frees the team from wasting time using and configuring local infrastructure, allowing them to stay focused on the project itself.

It is important that solutions are designed from the start with scalability in mind, and with high availability, considering the need for more resources over time, as well as the costs involved. Also, the trend is that most standard infrastructure such as databases will already be cloud-native.

Among the advantages of Cloud services, the main ones are Scalability and low cost. Another great benefit is that it allows you to test, prototype, and create a quick MVP to demonstrate capability before committing to a large-scale project.

Conclusion

The Analytics team must stay close to those who will ultimately use the data they provide because these are the business areas that have the questions that need to be answered. At the same time, the business must stay close to the Analytics team because it will get the answers from them. 

If you want to ensure the success of your analytics project, you must also have the necessary stack and expertise on your team, with a data culture that operationalizes them quickly, and not depend on on-premise infrastructure to scale your project.

At Kranio we help our clients achieve results and avoid committing these sins that could cost the success of the project:

• You are doing the project without involving the business user
• Your team is formed for an IT project
• You don't have a data culture
• You don't have a clear definition of your stack
• You depend on on-premise infrastructure
  

Ready to take your analytics and Big Data project to the next level?

At Kranio, we have a team of experts who will help you avoid these common mistakes and implement effective solutions for the success of your data initiatives. Contact us and discover how we can collaborate in the success of your company.