The 2021 Dresner Analytical Data Infrastructure Market Study is out and there are some fascinating insights into what the market wants when it comes to analytics platforms. It also shows how Exasol is rated for overall credibility and customer experience. Hint: very well!
This blog digs into the top requirements buyers have for their analytics platforms, including performance, Loading...advanced analytics capabilities and cloud deployment options.
Here at Exasol, we’re laser-focused on serving our customers. And while a lot of technology providers might say that, it’s different when your clients unanimously agree. That’s why we’re delighted to have earned a perfect recommendation score in the Dresner Analytical Data Infrastructure (ADI) Market Study for the fourth year running – with 100% of our customers once again reporting they’d recommend us to others.
That’s big news – and we really appreciate it. Understanding what makes our customers happy is crucial to our success and helps to create a feedback loop that allows us focus on what they value. The insights offered by the Dresner study sheds light on exactly what’s driving the market in terms of investment priorities, use cases, selection criteria and deployment choices. So, let’s dig in.
1. Performance is paramount
When asked what respondents’ overall selection priorities are for selecting an analytics platform, more than 80% of organizations say performance is the deciding factor. We’re not surprised by this finding, since selecting an ADI platform is not a straightforward task, and ‘performance’ spans a multitude of requirements – including support for diverse analytical use cases, dynamic workloads, fast response speeds and flexibility in deployment options. The platform needs to be future-proof too, to account for data and organizational growth and next-generation workloads.
The data sources and data types able to be handled by the platform also play a big role in the decision-making process. A large majority, almost 85%, say analytics workloads and workflows based on transactional data sources are the priority for them. This has remained consistent for the last four years.
SQL data capabilities are by far the highest data model priority for organizations considering an ADI platform, and more than 50% of respondents indicate Loading...in-memory is a priority, which is consistent with previous years.
This is most likely due to the demand for faster access to ever-expanding volumes of data across a diverse range of use cases – including those for Loading...real-time analytics, Loading...advanced analytics and predictive modelling – and the need to support a diverse range of users and applications.
Being able to ‘scale up and scale out’ is ranked as the top-rated development feature, with more than 55% of respondents viewing it as critical or very important. We believe this stems from organizations’ need for flexibility in their data architecture to support growing volumes of data without the need to sacrifice performance.
2. Evolving toward Loading...advanced analytics
It’s interesting to note that when it comes to use cases, Loading...data science is a high priority for over 50% of respondents to the survey. While it’s clear from the report that use cases for traditional BI, such as reporting and dashboards, continue to be important for analytics platform use, it’s also possible to see the ground is shifting. Buyers are demanding far more from vendors in terms of Loading...advanced analytics and Loading...machine learning.
Research suggests Loading...advanced analytics and Loading...data science is helping organizations achieve new levels of foresight and performance. It’s becoming central to solving complex, data-rich business problems such as dynamic pricing, fraud detection, customer behavioral analysis and customer churn prediction.
Therefore, we’re not surprised that the study found that sectors with customer- or patient-driven outcomes – such as financial services, healthcare, retail and wholesale – rate Loading...data science as their highest ADI use case priority, compared with other industries.
We believe this is also prompted by growing analytics maturity, larger datasets and easy access to higher-performance compute platforms, such as Loading...in-memory databases.
When looking at the analytical and development features important to Loading...data science, it’s worth noting that support for R, user-defined functions and Loading...machine learning are rated highly. Similarly, a higher-than-average priority is given to support foLoading...r programming languages such as Loading...Python and concurrent use licensing models. We believe this reflects a desire to utilize popular, lower cost and dynamic languages such as Loading...Python and R to help accelerate access to Loading...advanced analytics across a growing number of users.
3. Choice is key when it comes to the cloud
In terms of deployment, survey respondents view cloud as their highest priority in this year’s survey. However, it’s interesting that those from Marketing and Sales, and Business Intelligence Competency Center (BICC) organizations do not have this preference, with Sales and Marketing preferring a hybrid model and BICC rating cloud and on-premises deployment equally.
Our reading on this is that some analytics use cases are predisposed for as-a-cloud services, whereas others are better suited to on-premises deployment. As a result, multiple buying centres end up creating a hybrid deployment model for ADI platforms, which, according to the survey is a preference that increases with company size.
It’s our belief that many organizations have a hybrid model by default, especially as they start or continue their transition towards the cloud. This perhaps explains why cross data center integration and management for cloud and on-premises is becoming a growing priority for respondents.
Overall, the study reflects a strong preference for data platforms that are future proof, those that can manage existing and more conventional workloads but support the range of innovation and diversity of use cases needed, all the while managing this at scale.
The bottom line is, when an analytics platform is put to the test, flexibility, choice and performance really do matter.