Leaders aiming to create a Data Centric Organization strive to empower business personnel with data, predictive models, generative AI and data visualizations to enhance decision-making. Their goals include making smarter decisions with positive business outcomes, speeding up responses to opportunities, ensuring safer decisions that minimize risks and implementing change management to increase the use of analytics tools across the organization.
Additionally, they seek scalable solutions with advanced machine learning models, artificial intelligence capabilities and new data assets, all while ensuring data compliance, protection and security. Wayne Jackson, Sonatype CEO said, “To out-compete, you must out-innovate your competitors, which relies on making quick and effective decisions. Leaders need a full picture to make informed decisions, and gaining that level of visibility requires comprehensive data. But data alone won’t improve or accelerate the process and you must be able to make sense of that data.”
In this article, you will learn about steps you can take to improve analytics for data centric organizations.
How To Enhance Analytics For A Data Centric Organization?
Here are some steps you can take to enhance analytics for data centric organization.
Focus on End Users and Decision Flows
Most businesses are so busy in collecting and analyzing new data that they end up neglecting the end users. As a result, they never review their workflows and discuss the actions that analytics can help with. Soumendra Mohanty who is the chief strategy officer at Tredence highlighted this issue when he said, “Historically, the way analytics has been developed was to start with well-organized data, slap a bunch of well-thought-out algorithms to it, review what the data confesses, and expose recommendations in the form of visuals.”
He further adds, “This approach misses capturing input from the end user who will make decisions in their daily activity, whether it is an inventory manager, a campaign director, or a factory warehouse foreperson and is looking for real-time recommendations and directives on an hourly basis to put them into action.”
Clearly Outline Data Quality Requirements
Data quality can play a crucial role in success and failure of a business especially in the age of generative artificial intelligence. Successful businesses are aware of this and bolster analytical capabilities with data integration and fulfilling data quality requirements.
Irfan Khan, President and Chief Product Officer at SAP HANA databases and analytics, said, “Only with a strong data foundation and a unified view of data across their complex landscapes are businesses empowered to facilitate fully digitized business processes and seamless data exchange across their enterprise. Without clean business data, most AI-derived information cannot be trusted or effectively used.”
Learn How To Make Decisions Quickly
In today’s fast paced and highly competitive business world, you not only have to make decisions quickly but also make the right ones as well. This would only happen when you make data easily accessible for key stakeholders so they can process and analyze it quickly. Any delays in decision making can give your competitor an opportunity to gain the first mover advantage.
Prioritize Data Protection
Instead of considering data protection as a second thought, you should make it your top priority. When you integrate data protection from the early stages in the process, it gives you an opportunity to take feedback from key stakeholders such as end users about data security best practices. You can tweak things in light of their feedback. Let’s say you buy virtual private server to store sensitive data, you need to guard that server.
Scale Data Governance Programs
If you want to take full advantage of artificial intelligence, you must have data governance programs in place. A data governance tool for scaling data driven organizations is data catalog. A data catalog can help you implement access policies, configure authorization and enable discovery.
Emily Washington, Senior Vice President of product management at Precisely said, “Data catalogs that provide robust data governance and proactive quality monitoring drive confident business decisions. Given the heightened risks of ungoverned or inaccurate data in the AI era, prioritizing data catalogs that empower users with a comprehensive understanding of their data and its underlying health will enable them to harness data effectively, driving revenue and increased profits through confident reliance on business decisions derived from AI and advanced analytics.”
Create and Implement Standards
Creating implementation standards often falls under data governance but extends to tools, development lifecycle, testing, deployment, documentation and usability standards, crucial for data-driven organizations. A standards playbook can accelerate delivery, scale best practices and set deployment requirements.
Marty Andolino from Capital One emphasizes the importance of data standards in ensuring integrity, ease of use and security, advocating for embedding these standards into self-service experiences to empower users.A style guide for data visualizations and robust testing of analytics tools, dashboards and machine learning models are also critical, with Giovanni Lanzani of Xebia Data recommending end-to-end testing to catch issues early.
Larger enterprises should define comprehensive data management and architecture standards to handle large-scale datasets. Aislinn Wright of EDB suggests adopting unified data platforms with open standards for rapid project deployment. Simplifying data access and discovery is essential, as Krishna Sudhakar from Pricefx notes, to avoid grueling efforts in utilizing data spread across multiple systems. Daniel Fallmann of Mindbreeze advocates for semantic indices and automated metadata management to enhance data discovery and understanding.
Foster a Data-Driven Culture
To truly transform into data-driven organizations, digital trailblazers must cultivate a culture that embraces ongoing evolution due to advancements like generative AI and real-time analytics, which enhance decision-making capabilities. John Castleman, CEO of Bridgenext, emphasizes the importance of dismantling silos between business units, functions and technologies such as buy dedicated servers to improve communication, collaboration and information sharing.
A practical starting point is to frequently demonstrate new analytics capabilities and their business impacts across the company, showcasing successful use cases to alleviate fears and encourage adoption. By prioritizing end-users, building trust in data, evolving data governance and refining implementation standards, organizations can achieve competitive advantages and drive cultural transformation.
Which of these steps do you take to improve data analytics? Share it with us in the comments section below.