Democratizing Analytics Through Automation


Businesses from every industry are eager to leverage analytics and turn their raw data into impactful insights. However, outdated processes and technologies often prevent data users from taking full advantage of enterprise data.

Modern businesses need the power of data analytics to track competitor and customer behavior, measure and monitor organizational performance, and uncover critical market trends.

Organizations utilize data to make informed and strategic business decisions for customer retention, streamlined operations administration, and better revenue and growth. Without an effective data management strategy, companies can’t stay competitive in today’s fast-paced digital world.

Many organizations across the globe fail to take full advantage of their data, because of difficulties preparing data for analysis. If organizations want to create an insight-driven business, they must first establish effective data preparation and analytics processes.

Why is Data Preparation Such a Universal Challenge for Businesses?

Data preparation is the process of gathering, combining, cleaning, structuring and organizing data so it can be analyzed as part of data visualization, analytics and machine learning applications. Though business users are increasingly engaged in data analysis to support business growth, many organizations still designate IT as the go-to team for data requests and analysis. When IT resources are limited, a data request backlog can quickly build, impeding business users from receiving timely information.

Adding to the challenge are overly technical, often manual and time-consuming data preparation and analytics tools and processes. Business users don’t typically possess the necessary skills required to leverage highly technical tools, and even skilled IT resources can spend the vast majority of their time simply preparing data for analysis.

With ever-increasing quantities of data and a growing need to produce analytical insights, businesses must transition to an agile data preparation model to eliminate data backlogs and enable a broader range of data applications.

Creating a Streamlined Data Preparation and Analytics Model

The need for analytics is growing at an exponential pace. Data backlogs, combined with outdated data preparation and analytics tools, leave data consumers waiting weeks for the information they need to conduct timely analysis. Delays have a real impact on the success of an organization. For example, a sales team needs pertinent customer information to make the right offer at the right time in the sales cycle. If IT can’t get them the details on time, they could miss a prime sales opportunity. Instead, organizations must empower business users to engage in data analysis via a self-service model.

Self-service data analytics enable business users to analyze data sets without the need to learn technical languages or master complex tools. By incorporating both metadata management and data quality in the data preparation and analysis process, companies speed up time to insights and generate improved analytical outcomes.

Business users are empowered to easily find and combine data sets from unrelated source, quickly prepare and analyze data for timely business intelligence. Machine learning algorithms further augment users’ ability to uncover meaningful patterns and insights through automated data preparation and analytics processes.

This rise of self-service data analytics solutions and strategies benefits organizations of every size and industry.

Rapid Data Preparation in the Banking Sector

One of the best examples of greater efficiency and data value through modernized data prep and analytics comes from a large consumer bank that serves millions of customers across thousands of branches.

Teams from sales, marketing, legal and other areas all produced multiple data requests from centralized IT resources daily. Typically, requests were fulfilled in three to six weeks because of IT data requirements, time to obtain data access approvals and use of expensive, highly technical resources. As a result, by the time requests were completed, the information was outdated and irrelevant.

By establishing the processes and technologies to enable self-service data analytics, the bank implemented an automated process for logging data requests and employed an agile method for provisioning results.

Now, if there are multiple requests for the same information, data reports are stored in reusable libraries so subsequent requests no longer require recoding or any development work. The results are a self-service model where business users can run their own data flows.

When business user’s self-service their own data needs, they receive timely insights that are relevant to their work, benefiting the entire enterprise.