Enterprises across the world are struggling to make sense of their growing volumes of data. The Big Data market is expected to become $56 billion in revenue in 2020. Data science is helping enterprises integrate data from across the organization and uncover actionable insights to improve business decision-making.

From improving forecasting accuracy to understanding customers better, detecting fraud in time to driving prescriptive analytics – the use cases of data science in the enterprise are many. However, the success of your data science initiative is a lot more than the amount of data you collect or the tools you use to process that data. It all depends on your approach towards data discovery and data preparation – the two most critical phases of any data science initiative.

Why data discovery matters

When it comes to driving value from data science efforts, every person in the organization – whether a business owner, analyst, software developer, or program manager – needs to be able to read, understand, and glean value from all the information that’s coming in.

Data discovery is the process by which organizations can detect patterns in data with the aid of advanced analytics, thus enabling consolidation of all business information.

By applying skills in data relationships, data modeling, and guided advanced analytics functions, data discovery helps to reveal patterns and get all the insight that the data has to offer. Using interactive data visualization techniques, decision-makers can view information at a glance and understand major trends as well as spot outliers – in an instant. Because data is presented in charts and graphs on one page against being buried in tables spanning multiple pages, it makes it far easier for people to absorb and act on data.

Data discovery makes it easy for everyday users to make sense of data and find answers to questions without relying on the IT department to set up complex data environments. It makes it easy for them to handle a high volume and variety of data while greatly reducing time-to-insight. This ability to analyze patterns and trends within data sets can help businesses meet business goals, ensure success, remain relevant in the digital era, and gain a competitive edge.

Data discovery also paves the way for more sophisticated and pattern-oriented data analysis, helping organizations to overcome the challenge of providing ready-to-use statistical functions to business users and deliver proper outcomes – without having to write a single line of code. It goes beyond mere monitoring of organizational performance and extends capabilities to optimizing business processes and fueling new business models.

Why data preparation matters

Given the various types, formats, and volume of data that is accumulated across enterprises today, preparing or pre-processing data to improve reliability, consistency, and accuracy of data is critical.

By consolidating, cleansing, and transforming data, organizations can more easily connect to one or many different data sources while cleaning, reformatting, or restructuring dirty data. Since such pre-processing of data is extremely time-consuming, data preparation greatly reduces the time it takes to discover insights.

Since organizations end up collecting a lot of data – all of which isn’t needed for data analysis – it can skew the model for predictive analysis. Data preparation helps in effectively managing the volume, velocity, and variety of data. By merging relevant datasets into a new data set, filtering, cleansing, and aggregating it into the right format, data preparation eliminates faulty, irrelevant, or erroneous data, enriches it further, and improves the accuracy of data science initiatives.

Using analytical or traditional extract, transform, and load (ETL) tools, organizations can effectively integrate a variety of data sources and cleanse and transform it by adhering to data standards. This can only help organizations not only unravel insights faster, but it can also help them drive value sooner – across the enterprise.

Generate real business value

In today’s digital age, the right business decisions are the consequence of analyzing the right data. Therefore, all business users need to be able to access and make sense of the data they’re handling. Given the volume and variety of data being assimilated, applying data science techniques to all of this data is not only a time-consuming process but the analysis results that follow also have a high chance of being incorrect.

Focusing on data discovery and data preparations is extremely important to efficiently integrate data from various sources and cleanse and transform it to generate real business value. These phases not only enable data-driven decision-making but also help in enhancing business outcomes while propelling intelligent business strategies.

Author: webhero