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Doing More with Less: 5 Ways Leading Organizations Maximize the Value of their Data

Lisa Stewart
Lisa Stewart
17 novembre 2022 4 min de lecture

“Doing more with less” is a familiar refrain echoing through the halls of many organizations as they navigate economic uncertainty, supply chain disruptions, employee turnover, and changing customer experience needs. To answer this call, organizations are searching for efficiency gains and turning to data to unlock cost savings. 

Harnessing the power of data is critical for faster innovation and increased productivity. A recent Forrester Consulting study found that data-driven businesses are 58% more likely to beat their revenue goals than their non-data-driven counterparts.

Teradata’s deep experience, working hand-in-hand with our partners, to deliver analytics for hundreds of the world’s largest enterprises provides us with a unique perspective on how top businesses unlock data insights to drive productivity, efficiency, and growth. Here are five ways leading organizations maximize the value of their data to do more with less:

1) Integrate data without replicating data. Partial data sets give partial answers. Integrating information from various data types and sets is critical to enabling fully informed decision making and gaining a 360-degree perspective of customers and the business. 

“Data has become more and more important,” said Robin Tichler, IT Manager at Intertoys. “We’re now learning not only from data that we have, but in the future, we will also use outside external data so that we can see relationships we normally don’t see. For example, a sporting event or a television show’s success and the effect these activities have on product purchases.”

Analytics platforms that utilize in-database processing can provide significant performance improvements and deeper insights over traditional analytics approaches by blending and analyzing large data sets without moving them out of a database. The easy connection and analysis of data within low-cost object stores, rather than pulling data into a separate environment, cost-effectively delivers on the promise of unified data intelligence. 

2) Empower teams to query data rapidly and cost effectively. Rapidly querying large data sets and performing what-if scenario analysis is critical to improving efficiencies and prescriptively identifying cost savings. However, horror stories abound of a data analyst accidentally costing a company $100,000 or more with a poorly constructed query against a large data set. How can businesses analyze data but minimize the risk associated with query costs? Select analytics platforms, like Teradata VantageCloud, have a low cost per query and protections in place to ensure predictable performance and prevent experimentation from impacting existing quality and workloads.

“We need to ensure that data is available everywhere to unlock faster and better decision making, leveraging both internal and external data, structured and unstructured data to support human decisions with machine learning and prescriptive analytics capabilities,“ said Andy Hill, Global Vice President, Data & Analytics at Unilever. “Unilever runs 27 business services on our Teradata VantageCloud platform leveraging over 230TB of data supporting finance, sales, supply chain and HR. This enables approximately thousands of users to run hundreds of reports thousands of times each month to make the business decisions needed to run our organization effectively.”

3) Centralize data governance and control. The recent pandemic highlighted the need to centralize data governance across functions to rapidly respond to unplanned events. Organizations that centralize governance can ensure data quality and be highly responsive to changing market conditions. Analytics platforms that deliver easy-to-use management tools for self-service provisioning, computer/storage utilization tracking, database monitoring/alerting, backup/restore functionality, and disaster recovery reduce the risk of unforeseen costs and help organizations be more agile and responsive to change.

4) Unlock economies of scale through data elasticity. To do more with less, organizations need to carefully manage resources to meet demand. When it comes to data, this means selecting an analytics platform that elastically scales compute and storage resources to handle peak workloads. It also means giving users the ability to prioritize their most critical workloads by configuring business rules that ensure the right resources are dynamically applied to the right workloads at the right time. Data elasticity ensures organizations achieve economies of scale and keep costs low by using and paying for only the resources they need. 

5) Leverage the cloud and a connected data ecosystem to reduce software costs. One effective way to do more with less is to reduce your organization’s technology stack. Migrating data analytics solutions to the cloud can keep your number of software vendors to a minimum and deliver software as a service (SaaS). Selecting cloud-based analytics platforms that are open and connected with many of today’s leading data services and AI/ML modeling tools provides the flexibility to do more with the systems your organization already knows and loves.

Leveraging connected, cloud-based analytics platforms helps you harness data to identify new ways to  do more with less. Change can be uncomfortable but maximizing your data lets you pinpoint areas for productivity improvements and shape your organization’s transformation to a more efficient future. 

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