The Evolution of Data Preparation and Data Analytics

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The Data Analytics market, as we know it, is about to be disrupted (again). The new year will evolve from full Self-Service Data Preparation and Analytics with a governed, collaborative Enterprise Data Intelligence platform with an integrated Data Marketplace and Enterprise Data Preparation, that will result in improved business operations and processes. Though the change may seem abrupt, it is the latest swing on the Analytics pendulum.

A decade ago, data was locked down and managed by the IT team. If a business user wanted access to the Business Intelligence system or a database, a request was made and the IT team provided a report. Although data was fully governed, the information contained in the reports was outdated and hardly useful for insightful Analytics. More recently, the pendulum has swung in the opposite direction with Self-Service Analytics and Data Preparation. Business users are granted access to either the company’s datasets or leverage files and spreadsheets, allowing for real-time data use and Analytics. The problem is that individuals are working in silos and saving files anywhere; thus, breeding mistrust among colleagues about the data’s accuracy and origins. Enterprises remain hampered but, this time, by over access.

In 2018, the balance will be restored. Governed, yet collaborative Data Preparation and Data Analytics will combine business users’ need for agility and data access for analytical purposes with IT’s desire for with governed, secure processes to manage data usage. Enterprise, team-based Data Preparation and Analytics will give companies the agile, intelligent infrastructure for true data-driven decision-making.

Firms are already implementing integral parts of this new approach to Data Preparation and Analytics, including:

  • Data Marketplaces: By building a single repository that centralizes all data sets, users can quickly access information needed for their analytical projects. Meanwhile IT retains governance control by tracking who is using what information and when.
  • Smart Recommendations: Machine Learning algorithms deepen the centralized marketplace from an ordinary repository to a smart learning system that can make recommendations based on selected datasets. Recommendations are influenced by user behavior, so suggestion examples may include which datasets to use, proposed data models or sharing of the results of previous analytical outputs.
  • Collaboration: In addition to the smart recommendations, users can easily collaborate for cross-business analytical outputs. In today’s organizations, individuals can sit in side-by-side cubicles working to solve the same data problem. Under a team-based approach, users combine their knowledge by working together to answer questions and solve problems allowing for a fuller understanding of how the information fits together; thus, fully eliminating the issue of isolation.
  • Socialization: Individuals can rate datasets and outcomes in the centralized Data Marketplace to alert others on the usefulness of the information by giving thumbs up, thumbs down, or by suggesting a certain data set to colleagues through comments. This socialization further drives the team-based approach to Data Preparation and Analytics.

With the collaborative approach to Data Preparation and Analytics, companies implement a natural form of Data Governance over the data, and the centralized marketplace of curated data improves overall Data Quality and trust in the final analytical outcomes. As a result, enterprises create an intelligent Data Management platform that will ensure positive business results and operational processes.

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