The urgent need for real-time data has prompted companies to accelerate and expand their investments in data and data management solutions during 2020. While much of the industry focus is rightly on new developments in predictive analytics that drive business value, it is becoming increasingly clear that engineering teams are working And data is just as seriously empowered to empower this data with a more modern data structure and new datasets. Recent research by IDC, commissioned by Qlik, reveals that in the first quarter of 2020 alone, 30% of organizations made significant architectural changes and 45% added new data to their analytics environment, significantly expanding capabilities in their analytics arsenals. Their own. Multiple cloud platforms, on-premises sources, and even offline documents, such as direct mail surveys, all feed the input into the modern data pipeline.

All of this new data doesn’t translate into value or impact all the time. Data engineers and data analysts struggle to unify all of these data sources into a powerful insight-driven pipeline. Many spend up to 80% of their time trying to learn and evaluate data, leaving little time to turn data into business-driven insights. Creating valuable analytics requires that we turn this ratio upside down. Organizations need to be able to extract information at the speed that a competitive market requires to succeed. Efficient converting data into files active intelligence A state of constant intelligence from up-to-date, real-time information designed to drive immediate action – requiring companies to reconsider how they embed their data in a way that simultaneously makes it free to use analytics and protects the enterprise from risk. Here are tested strategies on how to develop onboard data to support real-time analytics and decision making.

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1. Align your data priorities with your business priorities

Every day there are important business questions to be answered – how many employees do we have and where are the employees? What is our expected profit this quarter? Where are our least productive business units? But not all of these questions are on the minds of senior officials. A good place to start the analytics journey is where analytics are created that drive the company’s most important strategic goals. The onboard data must align well with these goals. In short, strategic objectives need analytics, while analytics needs data.

Collaboration among this group of stakeholders, especially when it comes to data preparation, creates the critical organizational alignment needed to deliver high-quality data at the speed of innovation. By exploring data preparation strategies, teams at different levels and with different skills can build a 360-degree picture of data and how it is captured, stored, interrogated and leveraged. This understanding will shape smarter and more efficient strategies that can have a powerful impact on reducing processing costs by reducing the time in developing and capturing insights.

2. Focus on reuse

An aspect of the core DataOps frameworks that you develop is aligning data warehousing strategies with the use and value of data. In the first generation of business intelligence, complex transformations loaded data into data warehouses, resulting in subtle but fragile data transformations. This was fine for answering the same question over and over again but not so good for answering questions that are slightly similar. Then the pendulum turned dramatically into data lakes, making the raw data available to everyone, but left it up to the developer to understand what the data meant and how to use it. At the same time, traditional infrastructure that supports production platforms relies on batch loads and extended cycles, and has difficulty managing the data volumes, speed, and readiness required to use real-time data.

Modern pipelines find a happy medium, building data assets that are reusable, well-understood, and highly reusable by the enterprise. Techniques like Data Catalogs It can help clarify the inventory of these assets and extend the value of existing data storage investments.

With automated, real-time data lines, organizations can move toward greater efficiency by collecting and preparing the data they know they need and the data they may need based on business use cases in their data catalog. Leveraging the data catalog as a single repository increases the ability to do more analysis across the organization while still maintaining data governance and role-based policies, helping to ensure the right data is ingested and quickly transferred from different sources for more users.

3. Inclusion of data subjects in data preparation processes

The need to speed up the data pipeline to insights has never been more urgent, but it’s not just a technical issue. It requires clear business processes to ensure the team can keep pace with the new flow of relevant data. Understanding the most relevant data, based on roles and needs, means involving data owners in the process. The business owner can communicate the meaning of the data in the data set and determine the sensitivity or specificity of each one. Both are essential for effective and safe use in analytics.

Data catalogs add an element of technical support to this process by enabling anyone – from data engineers to business users – to learn, act, and respond to data that is relevant to them by seeing which sources are approved and most used by colleagues. As users explore and demand new data, this course adds to the extensive tribal knowledge of data that truly drives impact and value.

And when marketing customers implement enhanced analytics on trusted data sources, they are more likely to explore new questions, leading to the need for more new data. The catalog sees a need and serves new and relevant data sources for exploration. The data sets in the catalog now reflect a deeper understanding of the additional data that adds value to the process, which in turn will create value for the next user and the next campaign. Data catalogs are deployed in this way, as part of a searchable SaaS platform rather than a static data set, and support governance management, access privileges, and real-time data-driven decision making.

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4. Define, monitor and record each data flight

When business and technology leaders do not understand the nature of the data they own, they cannot make appropriate security and privacy considerations. In many cases, this leads to data security policies that create cumbersome data warehouses that limit the availability and value of the underlying data.

As data onboarding is enhanced through indexing and data ownership, leaders can create frameworks for security, governance, and metadata capabilities that protect user access privileges and track user activity while still making more data available.

A robust data preparation strategy has built-in policies to hide and secure personal and sensitive information. It should also include a set of safeguards to determine what will happen next, such as when an unexpected data type is encountered, or if a data field is incorrectly filled in.

Flexibility is important when looking to accommodate new data sources, which may come from unfamiliar platforms or new formats. Overly restrictive policy barriers must be reviewed to allow for these types of data sources to be included in the data catalog where appropriate – or risk potential road bottlenecks for meaningful insights.

Drive business forward with the right data strategy

The flow and complexity of data will grow over time, and how well the company harnesses this information and quickly turns it into insights that will separate the leaders in the pack. With strong data on board, organizations are in a better position to effectively embrace a culture of data-driven decision-making that includes non-technical users who share responsibility outside of data architects and data owners. As part of an overall DataOps strategy, data mining can enable organizations to efficiently manage their complex and growing data pipelines, harnessing the volumes of data they will need to make smarter decisions in the future.

For more information on the transformative power of DataOps, read This is the executive summary Or visit the Executive Insights Center at Click qlik.com/executiveinsights Get the latest trends to push your business forward.

Copyright © 2021 IDG Communications, Inc.

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