Organizations today acknowledge the use of data analytics, and are taking every step possible to integrate the full scope of the technology. The use of data in the present decade is not simply limited to the jurisdiction of data scientists or data analysts. Employees at different levels of the organization can leverage data for various assignments. In other words, data analytics as a technique finds its usage across several domains within an organization.
This rapid movement towards a data-oriented ecosystem has encouraged companies to promote a similar culture within their various business units. Enterprises are striving to make data accessible to employees at different tiers of an organization. This approach will not only spur the extensive adoption of analytics within an organization, but it will additionally introduce every employee to the very basics of applied analytics.
ANZ is one such organization driving significant initiatives to make its data more accessible to anyone across the organization for quite a while now. Reinforcing on the same objective, ANZ is currently encouraging its employees to become “their own little data scientist,” by furnishing them with easy-to-use tools. These tools are designed to help the staff draw value from the bank’s fast-growing data lake.
Darren Abbruzzese, General Manager of Data, ANZ remarks, “We’re trying to do that to make it real and take it away from those who like to play with Linux on the weekends and have [programming software] R installed on their desktop at work.” ANZ is stressing on making the contents of the data lake accessible to a wider business.
Data must be accessible to the average employee
Data is increasingly gaining significance and more decisions are being taken based on the analysis of data. It’s extending across every department of every business. People with data analytical skills will be in a better position to help their company excel. Data is surging and very soon everyone will be confronted at least once with a request to prepare, analyse, or interpret data. This is why enterprises are concentrating largely on promoting data analysis skills among the non-data trained employees.
Over the last decade, data analytics has become more of necessity than an added skill. The day is not far when companies would prefer professionals with data science skills over those lacking the same. The use of data analytics continues to proliferate, and it will soon become an indispensable technology. Making the data available and easily accessible to anybody across an organization is one way of encouraging employees to develop data analytical skills. Employees must be able to access the required data, dig necessary insights, and apply the same for solving business impediments.
ANZ is documenting the data obtained from the data lakes in a central registry to ensure wider use of the data. Anyone within the organization will find it easy to search the registry for terms like ‘mortgage risk data’ or ‘customer transaction data’, and this will help them identify the relevant files within the data lake. Abbruzzese comments, “A data lake for a person skilled in data science is a great idea, but for anyone else it could turn into an abstract affair. So, we are trying to make it more concrete by documenting what’s there and making it accessible.”
Simplifying the use of Data Analytics tools
Data analytics technique facilitates professionals in generating key business insights. But there is barely any value of utilizing such a technique, if it proves to be utterly complex. To inculcate data analytics skills within every employee, enterprises must ensure the tools they furnish are easy-to-use, and can be leveraged by an average employee across the organization. Learning the theories of data analytics might take some time, however, if the non-data trained professionals master the use of the basic analytical tools, an organization will surely excel.
Additionally, ANZ has been applying some “light modelling, joining and linking” on top of the data lake. The aim has been to improve its accessibility to other business units. ANZ has also furnished its staff with visualization tools as a part of an enterprise deal with data analytics software provider Qlik. The access to Qlik tools allows every staff at ANZ to develop some dashboards, or use the tools available and the models being generated to obtain real value. Abbruzzese adds, “The existing data warehouses will remain, particularly in slow moving and high risk functions such as regulatory reporting.”
ANZ’s policy engulfing the manage and use of the data is fundamental to how they deliver best experiences for their customers and build a world class digital bank. The sharpened focus on data became a necessity as banks are facing calls to open customer and transactional data via APIs. Abbruzzese had earlier expressed his fears regarding open data policies for the fintech industry.
Rise of Citizen Data Scientists
A large number of companies are striving to compensate the shortage of data scientists and data discovery solutions by automating tasks which would usually be executed by a trained data scientist, statistician, or an analytics expert. The confluence of trends has led to the rise of a new job role, a “citizen data scientist.”
A citizen data scientist doesn’t have to necessarily be an expert in the field of statistics and analytics. His primary role entails generating models that make use of predictive or prescriptive analytics. Essentially, they are the power users who use data-discovery offerings to automate parts of complex processes such as data preparation and pattern identification.
Citizen data scientist must be furnished with the appropriate tools, needed to execute their jobs. Though this emerging job role is on the rise, it doesn’t threaten the jobs of data scientists, data analysts, or business analysts. Infact, citizen data scientists will have to collaborate with other roles to derive the maximum value from analytics.