Data-driven organizations face huge challenges in getting the most value out of their data.
Businesses can see the data patterns but they can’t quite wrap their head around them and find meaning in them.
That’s because their data is highly disorganized and varied. According to IDC’s Global DataSphere research, humans generated 33ZB of data in 2018, and are projected to generate 103ZB by 2023. But most of the data that is generated today is lost – only 2.5% of all data is analyzed.
Furthermore, data is no longer stored in a mainframe database – it is distributed across hybrid and multi-cloud environments. There is not just unstructured data – throw in legacy data in the mix, and there is certainly a lot to think about when it comes to making sense of it all.
If the data is not administered quickly, that would lead to a bottleneck, increasing the time taken to process the data. Unattended data could also lead to degradation of data quality, which will render the data useless.
The misunderstood data scientist
A data strategy is not just about hiring a few data scientists and leaving them to run the show. They are often hired to solve complex problems but few people understand their abilities as well as their constraints.
To make the best of their data strategy, enterprises need to understand that data only brings value if it is presented to decision-makers at the right time and in the right context, leading to defined business outcomes and driving results.
Indeed, one of the most common frustrations among data scientists is that their work is underutilized. According to the IDC’s Advanced Predictive Analytics Survey, only 14 percent of organizations said that the output of data scientists is fully operationalized.
This means the work of data scientists are unlikely to reach its audience, who are executives, managers, or employees, all of whom are tasked with making business decisions.
Many enterprises also don’t realize that the data science process involves several steps, with 80 percent of data science teams’ efforts spent in searching and prepping data, rather than deriving value from analysis.
The data science workflow involves data acquisition, feature engineering, data preparation, tuning, model tuning, testing, training, selection, deployment, and monitoring.
Many steps in the data science workflow are manual and time consuming and should be enhanced with intelligence or automation.
Why analytics software will give your data teams a much-needed boost
Hitachi Vantara’s Pentaho platform aims to ease DataOps workloads by offering data integration and blending, using drag-and-drop tools, for instance, to create data analytics pipelines. That allows data scientists to organize where the data is to go and what kinds of processes it needs in a simple and easy manner.
The acquisition of Pentaho in 2015 helped to fulfill Hitachi’s strategy of delivering business innovations that integrate machine data, information technology, and analytics to distill value from big data and the Internet of Things. This acquisition is a transformational event for the big data industry, and will accelerate enterprise adoption of big data technologies and solutions through easier, faster deployment, leading to faster ROI.
For instance, NASDAQ sought to revamp its legacy systems with a cloud-based platform that also uses big data integration and analytics. Its end goal was to create new offerings using the data in order to differentiate itself from other stock exchanges.
The company needed a way to quickly process and transform large data sets coming from multiple sources into a format that could be used efficiently to analyse several business lines in a timely manner. And as the owner of eight European stock exchanges, on top of its own NASDAQ stock exchange, this proved difficult due to the sheer amount of data, with NASDAQ collecting a100 billion rows of financial information every day.
NASDAQ utilized Pentaho to create a scalable, cloud-based big data integration and analytics platform. By embedding Pentaho, the NASDAQ OMX enterprise deployment is faster and more powerful for users reducing costs by half of the company’s previous solution.
At the end of it, it is not just the employees that stand to benefit – with data science, developers can make applications that use insights from data science to differentiate businesses, such as improved customer relationships or personalization of product offerings.
The challenge, however, is to overcome the status quo and get past issues of being “too busy to change” or that analytics software is not on the company budget.
Investing in a platform that can provide support of the data science workflow, automate manual steps, and analyse the data for valuable insights will allow enterprises to realize the power of data science fully.