When your data is trusted, high quality, and able to reach the right people at the right time, it can help your organization capture new market opportunities through leading-edge customer experiences, higher campaign conversion rates, and AI-driven supply chain decisions.
However, according to Experian’s 2020 Global Data Management Research, 78% of businesses report they struggle with data debt — the accrued costs of not having well-governed and trusted data — and encounter obstacles such as delays in insight and a lack of certainty about underlying data. To turn data from burden to benefit, businesses must adopt a clear DataOps strategy. DataOps is an orchestration of people, processes and technology designed to make data business-ready.
Read the ebook “DataOps can accelerate your journey to AI” to learn more.
The following three strategies use DataOps practices and solutions to help monetize your data — either by bringing in new revenue or producing cost savings.
1. Become AI-ready with a faster, more efficient data pipeline
To create a foundation for AI, you need to be able to quickly access large volumes of data, often spread across different deployments. But to do so, you must be able to integrate data at high speeds with a breadth of delivery styles, including data transformation using extract, transfer, load (ETL); data replication and data virtualization.
For example, when a regional US retailer with 300 locations wanted to guide its supply chain management decisions with AI, it first needed to extract data from on-premises Oracle systems and move it to a public cloud at least seven times faster. The retailer turned to a best-in-breed parallel engine that can spin compute resources up or down on demand, while providing automatic workload balancing to maximize throughput. The IBM® DataStage® data integration solution was able to meet and exceed their target.
A major European retailer had a similar data pipeline challenge: it wanted to use AI to predict a customer’s next purchase, but it first had to analyze enormous volumes of data being generated daily by its 40,000 employees in 60 countries. The company implemented a big data integration, quality and governance solution that reduced processing time by 95%, impact analysis time by 99%, and inventory analysis time by 83%. See the DataOps ebook (p. 18) to learn how a pioneering Hadoop solution and combination of IBM DataOps tools is helping to keep the retailer’s customers happy.
2. Get a 360-degree customer view to enhance growth and compliance
At many organizations, siloed data makes it difficult to provide a single, unified customer experience. Using IBM Master Data Management however, an organization can create a single, trusted 360-degree view of each customer’s interactions, preferences, and status. As a result, it can provide personalized treatment, options and customized interactions that provide a level of individualized service beyond what customers expect. Customers can also control how much of their data they wish to share or not, enhancing compliance.
Once data is made business-ready, it can also help an organization generate offers that are customized for their customers. A modern data catalog like IBM Watson® Knowledge Catalog can help organizations view, find and manage large amounts of data, automatically generating metadata and helping team members share insights that can improve the business.
This approach helped Italian retail bank Credito Valtellinese, which had been hobbled by internal systems that were not centered around client relationships. It had to create an analytical foundation, including a data catalog, to better understand customer behavior and needs. When the team succeeded, the solution delivered precisely targeted promotions that increased campaign conversion rates by 10%. Learn more about this project in the ebook: A comprehensive guide to the modern data catalog (p. 13), and get an overview on how a data catalog enables consistent definitions, self-service management and enhanced use of data.
3. Expand access to quality data
In addition to creating an efficient data pipeline by connecting and integrating data across environments, it’s critical to ensure the data itself is high quality. Organizations need to trust data before they can use it. Only quality data can yield new customer insights and opportunities to delight customers.
One way to help ensure data quality is with a solution such as IBM DataStage. It runs validation rules automatically as part of the ingestion process, helping to reduce silos and ensure that meaningful and valuable data is available for use in near-real time. Cataloging solutions like IBM Watson Knowledge Catalog also help operationalize data quality by enabling teams to track data lineage as well as collaboratively score data on quality.
Keeping data quality high is a key objective at South Africa’s Standard Bank Group, so that it can use its data to provide more contextually relevant engagements with its clients. A company-wide effort using IBM DataOps solutions improved data quality 92 percentage points, from 6% to more than 98%.
Standard Bank Group serves clients across Africa with DataOps
See why IBM is a leader in the 2020 Gartner Magic Quadrant for Data Quality Solutions.
Getting started with DataOps
Growing a DataOps culture and developing best practices are key steps in building value and competitive advantage. By doing so organizations can resolve untrustworthy data, connect siloed information, and better manage records to make more informed decisions.
IBM Cloud Pak® for Data is an all-in-one cloud-native data and AI platform that enables you to take advantage of a broad set of DataOps capabilities and integrate them quickly into applications to accelerate innovation, including modern cataloging and data integration.
Read the eBook “DataOps can accelerate your journey to AI” to learn more.
Or schedule a free one-on-one consultation with an IBM DataOps expert.
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