3 Information Preparation Features Recommended Via Trade Analysts
Data preparation tools have gone through a series of changes. Long back, preparing data for analysis was considered a time-consuming process. It involved several complicated steps including, data Extraction, Transformation and Loading (ETL), required access to data marts and data warehouses, and entailed a multitude of complex messaging and manipulation of data across myriad data sources.
Such complicated ETL-oriented data preparation tools got replaced with self-service tools over time to give business users and data analysts the ability to explore, analyze, and transform data in a dynamically visual and interactive way.However, still, many were unable to meet the day-to-day data inquiry requirements of their ever-changing organizational dynamics and market paradigms, making organizations difficult to do business with.
As per Gartner’s market guide, to combat such challenges, self-service approach to data preparation needs to be married with data integration.This allows enterprises not only to thrive in a self-service environment but also to support data integration, analytics and data science use cases in production. Gartner’s2019 market guide for data preparationshows that companies willing to expand the reach and value of data preparation within their enterprise supports more than self-service use cases.
This research serves beneficial for helping data and analytics leaders understand how data preparation can impact businesses, their decision-making and success graph. Here are 3 key takeaways from the report that can help organizations use data effectively.
1. Data Management Turned into a Team Sport
Self-service approach commenced data preparation market undoubtedly. It became popular at a time when business users had no access to efficient methods to discover new data sources before they could extract useful insights, even after they were empowered with modern data discovery tools such as Tableau or Power BI. They depended heavily on IT or created data silos uncontrollably by relying on tools such as Microsoft Excel. Either way, IT productivity and revenue production got hampered severely. Modern data preparation tools changed the dynamics of business in its entirety. By addressing these challenges, such tools empowered larger audiences with its data integration and data quality management use cases and helped IT focus on the governance role instead of an operational role. In other words, modern data preparation solutions help companies create an integrated corporate culture to deliver accurate data-driven insights, thus boosting IT productivity and revenue.
As per Gartner’s Market Guide, with changing market scenario, the best way to reap maximum dividends from data is to turn data preparation into a team sport where everybody in the business and IT can collaborate to reap the dividends of data. All the enterprise-wide initiatives such as data integration, business intelligence, analytics, data warehousing, data science, or data quality management can benefit from this approach.
2. Users, Skilled or Non-Skilled, Empowered
Gartner’s 2019 market guide for data preparation also sheds light on how tools are embodying modern technologies like machine learning, pattern recognition techniques, data cataloging, etc. This enables, even less-skilled personnel, to perform tedious activities with their data while automating tasks such as transformation, reconciling, integration, or remediation as soon as they become repetitive. This rules out the misconception that data preparation is meant only for selected business users. Truth is, modern data preparation owing to its empowering nature is envisioned as a game-changer for all types of users whether skilled or an amateur.
3. Hybrid Cloud Model is the Key
In this report, growing customer demands for data preparation beingdelivered through innovative Platform as a Service deployment models are also mentioned. As per the report, “organizations need flexibility to perform data preparations where it makes the best sense, without necessarily having to move data first.” This can be achieved with a hybrid model – a model that embraces the best of cloud-based and on-premise integration approaches. So, whether cloud customers need to run their preparations on-premises or vice versa, modern data preparation tools can be of tremendous assistance.