Within almost every industry, businesses and organisations are swiftly adopting new data science technologies in order to take advantage of data analytics. It inevitably brings an increasing reliance on the importance of managing and analysing digital information.
There are many benefits of this adoption, including gaining insights from big data that may never have previously been discoverable, and having access to both consumer and behavioural system of work that were previously inaccessible under traditional methods of business. However, with this overall positive adoption, there comes the negative fact that much of the job has traditionally involved repetitive and sometimes unsuccessful tasks that lead to wasted time decreased productivity – never mind lowered job satisfaction!
Wasted time in the workplace has become a hot topic of discussion, and businesses are consistently vying to shape their workforce into a well-oiled maximum efficiency machine, increasing productivity and bettering overall company performance. And yet, the rising demand for data science has meant that the drive for productivity has taken a back seat as the demand for big data has grabbed the wheel and sent organisations careering into new directions.
This, however, is somewhat ironic as data science should, in fact, be showing its value by improving both performance and productivity for organisations, when deployed properly in the right data friendly culture. However, this has not proven to be the case for many organisations...
Whilst there is a continuum for those who ‘do analysis’ and there will always be a place for specialists, the people within the business do their job every day have the knowledge to do their jobs better, given the right data and the right tools. For the business analyst doing their job day in, day out, those equalisers come in the form of data. Knowledge workers will often do analysis as part of their job, and an increasing number are doing ‘advanced’ data science tasks – including spatial and predictive to bring increased insight and value to their business.
Employing both data scientists, who will provide the quality control and specialised management, and business decision maker analysts, who will have a better understanding and better access to the context of their business problems and any associated data challenges. This is where being able to use a self-service tool from throughout of a company enhances effective and efficient data analysis.
While the value of data professionals remains unchallenged, providing them with streamlined systems and methods of working remains problematic. This growing issue has prompted a study to be conducted by IDC, the research company. IDC’s Infobrief; ‘The State of Data Discovery and Cataloguing’, commissioned by Alteryx.
This, therefore, is empowering all knowledge workers to spend more time gaining valuable insights and extrapolating the most valuable data, rather than completing repetitive data-handing tasks. Businesses will once more be able to focus on streamlining their processes and focus on efficiency and productivity, having effectively adapted to the demand for data science.
The IDC study, based on a comprehensive survey of 400-plus individuals performing data functions across North America and Europe, found that data professionals spend 60 per cent of their time getting to insight, but just 27 per cent of that time is spent on actual analysis. Instead, 37 per cent of that “getting to insight” time is spent searching for data, and 36 per cent is spent just preparing the data in the first place. These methods of working result in data professionals wasting 30 per cent of their time. That equates to an average of 14 hours per week due to not being able to find, protect or prepare data. Furthermore, they waste another 20 per cent of their time – 10 hours per week – building information assets that already exist elsewhere in the organisation. This equates to a total of a 50 per cent loss in working time every week on unsuccessful activities or repeated efforts. Data professionals are spending more time governing, searching and preparing data than they are on extracting business value.
“It is evident that many professionals are not aware of what resources are available within data assets such as data lakes, how to access the data, where it came from or how to glean trusted insights”. Even though there is no doubt that data discovery and integrity is important for business, 30 to 50 per cent of organisations say they are not where they want to be. Data discovery is important to all aspects of business, from operations efficiency to compliance to risk reduction, revenue growth, and beyond,” said Stewart Bond, Director of Data Integration and Integrity Software Research at IDC. “Knowledge of how, where and why data is used, by whom, and what information already exists will help data professionals refrain from repeating efforts, increase personal productivity and free-up time for more advanced analytics.”
Organisations who are conscious of their data assets, and are opening them up to the business, will naturally become insights driven where it’s easy and natural to answer questions. And inevitably, where answers are findable, the insights are easily embedded back into the company’s business models in a virtuous cycle of learning, improving, and optimising. This efficient flow of insight, knowledge and learning is one of the key systems needed to allow the increase of productivity of data to remain efficient.
The inefficiencies of data intelligence and knowledge are costing U.S. organisations $1.7M per year for every 100 employees, and European organisations €1.1M per year for every 100 employees. There are solutions to these problems from the processes and procedures of traditionally managed data science. By utilising individually empowering end-to-end analytics platforms (rather than BI systems requiring coding, and specialised training) businesses will be able to gain deeper insights into their data while getting to the answer much faster than previously possible.
As the data age develops and grows, organisations are adapting and learning the best ways to structure themselves to maximise capabilities. With tools that allow workers from the national average business professional to the expert and well-studied data science specialist, there is a newly forming idea of integrating the entire workforce into the data analysis process, and providing solutions to the ever-changing (indeed, the sprawling) landscape that is big data.
Nick Jewell, Lead Technology Evangelist, Alteryx
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