Founded in 1982 as a construction company, client has successfully positioned itself amongst the top 10 construction & infrastructure management companies in India. Client has to its credit many prestigious projects in the Industrial, Power, Institutional & Infrastructure sectors across India.
Small and medium sized businesses (SMEs) often find it difficult to balance the day-to-day need for data with the cost of employing data scientists or professional analysts to help with forecasting, analysis, data preparation and other complex analytical tasks.
The use of business intelligence and business analytics is growing in every industry, business function and in companies of every size. 48% of small and medium sized business CIOs responding to a Gartner survey revealed that business intelligence (BI), data and analytics is one of the technology areas that will have the largest amount of new or additional spending.
If your business spends a lot of time and money on the task of extracting, transforming and loading data (ETL) and preparing that data for analysis, you might want to consider the advantages of self-serve data ET. Self-Service ETL can and should be easy enough for business users so that your business can enjoy the benefits of advanced analytics without hiring a team of data scientists or IT professionals.
How does an organization help the self-serve advanced analytics model grow and thrive? Responsibility lies in a number of places within the enterprise.
SSDP (otherwise known as self-serve data preparation) is the logical evolution of business intelligence analytical tools. With self-serve tools, data discovery and analytics tools are accessible to team members and business users across the enterprise.
Data prep can slow down analytics and cause delays. Self-service data preparation (when done right) can enable business users to leverage sophisticated, easy-to-use tools for self-serve ETL. Data extraction, transformation and loading (otherwise known as ETL) can be time-consuming and requires professional skills but self-service ETL will walk business users through an augmented data preparation process and take the complicated, confusing steps out of the process by helping the user make decisions on how to prepare, clean, reduce and use the data in the best way possible.
In years past, data preparation was the domain of IT professionals and data scientists. In order to prepare data for analysis, one had to find, gather, and prepare the data and that preparation included cleaning, combining, reduction and shaping of the data.
Self-Serve Data Preparation is a critical component of augmented analytics. If these terms seem foreign to you, just know that they represent the future of business analysis. As organizations adopt self-serve business analysis, the business user with average technology skills must be able to leverage tools that are sophisticated, yet easy to use.
Self-serve has many meanings. You can pump your own gas, you can serve yourself at a buffet, and sometimes you can even do your own data preparation. You will notice that I said ‘sometimes’. That is because you have to choose the right tool if you want to really participate in self-serve data preparation.
