What is hyperconverged analytics?

Today's highly dynamic business landscape requires all companies to be flexible enough to quickly adapt to emerging challenges. As the pandemic has shown us, the ability to react quickly to unforeseen disruptions can mean the difference between staying in business or closing your doors for good. This is why data analysis is now more important than ever.

Through data analysis, companies can become data-driven organizations that base all their decisions on solid information and insights. Furthermore, using the latest analytical tools, they can predict scenarios and prescribe actions to better face new and unknown challenges.

For this to happen, however, companies must adopt cutting-edge technologies in the field of analysis. Today, that means embracing hyperconverged analytics.

All analytical tools in one

Until now, data analysts had to rely on a suite of data analysis tools to cover as much as possible. Therefore, they had to adopt different dashboards, visualization platforms, cloud-based analytical tools and many other applications to have a slightly clearer picture of their own markets. With hyperconverged analytics, this has come to an end.

Hyperconverged analytics combines all of these different tools into one comprehensive solution. So data analysts can use data science, cloud-based tools, machine learning algorithms, and visual and streaming analytics from a central platform.

The best part of hyperconverged analytics is that such a solution can work with data coming from multiple sources and evaluate it with all possible analytical approaches: descriptive, diagnostic, predictive and prescriptive. This allows data teams to streamline their efforts and even tap into a whole new set of insights that might otherwise go unnoticed if they were using multiple data tools.

Benefits of Hyperconverged Analytics

Using a hyperconverged analytics solution offers several advantages beyond the mere convenience of having all data stored and analyzed in a centralized location. Some of the most important advantages include:

Richer Insights

By combining different analytics approaches, hyperconverged solutions can provide new, previously unnoticed connections and relationships between diverse data sets. This way, you can access deeper and more sophisticated insights for your business.

Faster insight generation

Using a combination of AI and machine learning algorithms with data cleaning capabilities, visualizers, and report generators, you can get insights faster in the same place you're using to collect the data. You can even develop an analytics solution that can evaluate data sets in real time.

Greater scalability and easier maintenance

By working with a single platform, you don't have to worry about integration or scalability issues. You just need to provide enough processing power to keep delivering results for all your datasets. Plus, working with just one system allows you to easily keep it up to date with the latest security protocols and patches.

Simplified operations with the same team

Expanding your data analysis efforts almost always means hiring more experts to handle the new tools. You don't need that with hyperconverged analytics. Your existing team can handle the centralized solution, especially since all the features and functionality you need will be included in the hyperconverged platform.

Reduced costs

While a hyperconverged solution isn't the cheapest option, it's certainly worth the investment. You may pay a little more upfront, but you'll get an impressive ROI and significant cost savings. That's because you won't have to worry about maintaining multiple solutions, paying for extra help, or losing business opportunities due to outdated analytics.

The path to hyperconvergence

Hyperconverged analytics is the next logical step in analytics. This is because data scientists and teams need more powerful and sophisticated data tools to work in today's highly competitive and ever-changing landscape. Therefore, combining multiple tools into a centralized hub seems like a natural evolution. See how we got here:

Non-digital tools

In the beginning, people performed analysis-related tasks with pen and paper on basic balance sheets. Any form of data analysis took this format, which was time consuming to provide very basic and mostly descriptive insights.

Computational analysis

When computers finally arrived in offices around the world, developers created the first analytics platforms using databases, mathematical models, and algorithms. Of course, these first attempts were quite limited, especially due to hardware restrictions.

Big data

The latest era has finally overcome the limits of databases and started adopting new technologies to provide new features. Thus, data collection, cleaning, analysis and reporting have become faster and easier, and the depth of insights has reached a sophistication never seen before.

Flexibility

Enterprise software often includes features that automate repetitive tasks, freeing team members to work on higher-level tasks and challenges. For example, an HR application can manage many scheduling, payroll, and recruiting tasks, allowing the HR department to develop new programs to support employees.

Hyperconverged analytics

Bringing together the power of multiple data analytics platforms, hyperconverged analytics is the next step for data scientists. Here, the goal is to streamline processes through centralized hubs while increasing the relevance and usefulness of insights through the more comprehensive use of AI algorithms.

When to use hyperconverged analytics

While hyperconverged analytics solutions are widely beneficial and can boost any business, the reality is that not all companies need to adopt them immediately. Small businesses or companies with well-oiled analytics efforts may not see its many advantages right away, which means they may wait for the best time to adopt a hyperconverged platform.

This doesn't necessarily mean that every other company should jump on the hyperconverged bandwagon. The best way to know if these solutions are for you is to look for these telltale signs.

• You have multiple data sources that need to be analyzed in as close to real time as possible.
• Your data volume is exceeding your infrastructure's current cleaning, analysis, and reporting capabilities.
• You're having issues with your data team, which means more and more data-related solutions are going unmanaged.
• You are missing out on a lot of business opportunities related to new demands and disruptions (this is even worse if your competitors are capitalizing on them).
• Your insights are superficial or don't offer enough depth for you to really make a difference in the decisions you make based on them.
• You are using too many data analytics platforms and the costs of maintaining them are taking their toll (costs don't necessarily mean money, but also time and effort).
• Your company is going through a process of digital acceleration that is often hampered by outdated data analysis tools.

You may be able to solve one or even two of these problems. But the most recommended course of action to avoid the friction these issues are costing you is to embrace hyperconverged analytics. This will completely transform your business and put you on the right path to becoming a data-driven organization.

Of course, adopting hyperconverged analytics isn’t a “buy, plug and play” kind of thing. You need to properly migrate your data, implement the solution, integrate it into your existing ecosystem, and configure it to get the most out of it. This is easier said than done, so it's only natural if you need help doing this.