Enterprise Data Technology, Part 2: Harnessing Data For Success

By Robert Kramer, Patrick Moorhead - November 13, 2023

The first installment of this series talked about internal and external data sources. This post dives deeper into the specifics of enterprise data technology. To kick things off, I’ll showcase how some best-in-class organizations are leveraging the full spectrum of their data resources to meet their goals and accomplish results that would otherwise be out of reach. Then we’ll look at the major components of EDT before discussing the business benefits of successful EDT implementations.

Top Organizations Across Industries Get The Most Out Of Their Data

In one industry after another, high-performing organizations understand the power of data for better decision making—and invest resources accordingly. For example, in the medical field, Mayo Clinic creates comprehensive patient profiles by integrating patient records, medical histories, treatment data and diagnostic information across all its departments and sources. That allows its medical professionals to make well-informed diagnoses, tailor treatment plans and enhance patient monitoring. A thoughtful approach to EDT also helps Mayo Clinic streamline workflows, reduce redundancies and foster collaboration among healthcare teams, improving patient outcomes and experiences.

Toyota illustrates how EDT can optimize complex supply chain processes—for one of the biggest supply chains in the world. Toyota gains real-time insights into inventory levels, demand fluctuations and production cycles by integrating data from suppliers, production lines and distribution channels. The carmaker can align its manufacturing with market demand, which minimizes excess inventory, reduces lead times and enhances supply chain efficiency.

Coca-Cola relies on EDT to fine-tune its distribution and sales strategies. The company adjusts its product offerings and distribution tactics by analyzing sales data, consumer trends and even weather patterns in real time. This agility enables Coca-Cola to effectively meet consumer demand, optimize distribution and ultimately enhance profitability.

The Components Of EDT

EDT includes a wide range of tools, systems and technologies that organizations can use to effectively handle their data and leverage it for essential and critical operations. Let’s explore some of the components of EDT:

  • Unified Data Architecture establishes a coherent framework to accommodate structured, semi-structured and unstructured data sources. This is fundamental for efficient data storage, processing and analytics.
  • Data Governance is maintained through a framework that enforces policies, procedures and standards to ensure data consistency, accuracy and security in line with compliance and regulatory requirements.
  • Security solutions and tools protect data from unauthorized access or use by employing encryption and other techniques.
  • Data Integration uses extract–transform–load or extract–load–transform methods. In the traditional ETL, data is extracted from the source, transformed and then loaded into a data warehouse or data lake. Conversely, in ELT, data is extracted, loaded into the warehouse or lake as-is and then transformed within that system. Additionally, API integrations can be used to create direct links between systems for real-time data exchange.
  • Data Storage solutions access different data repositories with distinct purposes, such as databases that manage structured data to optimize transactions. Data warehouses aggregate structured data from multiple sources for analysis and reporting. Data lakes store structured, semi-structured and unstructured data until needed.
  • Advanced Analytics includes tools such as business intelligence, predictive analytics and machine learning platforms. These are important for interpreting complex datasets, predicting trends, reporting on business operations and supporting planning. Additionally, data mining is used to identify patterns and relationships in large datasets.
  • Data Backup and Recovery preserves data for retrieval and restoration in case of natural disasters, catastrophic system failures, cyberattacks or other adverse events.
  • Metadata Management assists companies in handling elements such as data lineage, dictionaries, integrations, controls, validation, and cataloging. Also, AI models can generate and return new metadata to repositories and improve metadata quality. These steps can create new insight into an organization’s data and make it more usable.
  • Collaboration enhances teamwork on data projects by enabling the sharing of information, visualizations and models. Tools like Slack, Google Docs, Microsoft Teams, Asana, Monday and Trello facilitate communication within teams, promoting remote work, new explorations of enterprise data and greater innovation.

The Benefits Of Successful EDT Implementation

Organizations leverage enterprise data technology to harness the power of data and pave the way for future success. Here are several key areas where organizations apply EDT:

  • Decisions Powered By Data — Because it enables organizations to collect, store and analyze large quantities of data from many sources, EDT creates a foundation of crucial information to guide decisions for strategy, marketing, product development, business planning and customer service.
  • Business Intelligence — By tapping into advanced analytics, AI and machine learning, organizations can forecast trends, anticipate customer behaviors and understand market dynamics. These results help organizations capture opportunities and mitigate risks.
  • Customer Retention — Data-driven marketing strategies enhance the understanding of customers, optimizing acquisition and helping retain clients by analyzing details such as purchase history and feedback.
  • Operations — EDT allows organizations to optimize their internal processes and increase competitiveness by using data to identify bottlenecks, streamline workflows and reduce operational costs.
  • Innovation — Advanced market research and other types of information can prompt innovation by identifying areas where there’s a demand for new products or features. This can guide product development efforts.
  • Competitive Advantage — Organizations leveraging EDT can gain an edge as they respond quickly to market changes and adapt to customer preferences.
  • High Performance — A high-performance infrastructure ensures efficient data loads and user queries while supporting low latency, high throughput, parallel processing, robust security and accurate monitoring. This allows businesses to operate consistently, remain agile and adapt rapidly to changing conditions.
  • Compliance — EDT plays a critical role in ensuring compliance with data protection regulations as it allows companies to implement consistent data handling policies and procedures. By prioritizing data compliance, organizations not only shield themselves from risks but also prepare themselves to meet future regulatory requirements.
  • Scalability — As organizations grow, their data needs also expand. Effective EDT empowers organizations by offering not only efficient data management regardless of data volume, but also flexibility, automated scaling and cost-effectiveness. (In a future post, I’ll go into more detail about how cloud technologies fit into this picture.)
  • Strategic Planning — Data insights inform long-term planning, helping organizations to establish realistic goals and make well-informed investments in technology and other resources.
  • Crisis Management — During times of upheaval or uncertainty, EDT can offer critical insights that assist organizations in making swift decisions to navigate challenges.

Summing Up EDT In Modern Enterprises

Enterprise data technology is an expansive and perpetually evolving territory, one that exerts a pivotal influence on contemporary enterprises’ operational and strategic trajectories. As the significance of data escalates, the need for steadfast, scalable and secure EDT solutions will only increase.

Across many years of experience in enterprise data, I’ve encountered significant challenges related to data integration, especially when implementing new enterprise resource planning or supply chain management systems. Among the consistent hurdles, a few issues stand out, starting with the fundamentals of data quality and the availability of data at the required data field level. Many companies are hesitant to admit to having poor data that needs correction. Yet a candid assessment of data quality and completeness is essential for success. In some cases, it may even be necessary to start anew or manually reenter existing data to ensure its accuracy. On the positive side, this process can provide an opportunity to train those responsible for inputting data into the new system.

Another recurrent obstacle is the integration of various systems of record into a single repository. This is an ongoing challenge that often necessitates the use of APIs to ensure seamless connectivity across multiple platforms and systems. The process highlights the crucial need for strong data integration strategies. Such fluid data management is the cornerstone of efficient operations and informed decision making in today’s data-centric business environment.

For EDT to achieve true success, these elements, their functionality and the management of them need to be customized to each organization’s unique needs. Once that is accomplished, companies must stay the course with routine audits, instructional sessions and technological enhancements. Doing so will perpetuate the efficiency and security of EDT and ensure that it remains aligned with organizational objectives.

In my next installment on EDT, I will address more potential pitfalls and challenges and suggest strategies for overcoming them. As businesses embrace EDT for innovation and growth, proactive approaches are essential to overcome any obstacles that may arise.

Robert Kramer
VP & Principal AnalystatMoor Insights & Strategy| + posts

Robert Kramer is vice president and principal analyst covering enterprise data, including data management, databases, data lakes, data observability, data analytics, and data protection. Robert has over 30 years of proven experience with startups, IT companies, global marketing, detailed strategies, business modeling, and planning, working with enterprise companies, GTM assets, management, and execution.

Patrick Moorhead
+ posts

Patrick founded the firm based on his real-world world technology experiences with the understanding of what he wasn’t getting from analysts and consultants. Ten years later, Patrick is ranked #1 among technology industry analysts in terms of “power” (ARInsights)  in “press citations” (Apollo Research). Moorhead is a contributor at Forbes and frequently appears on CNBC. He is a broad-based analyst covering a wide variety of topics including the cloud, enterprise SaaS, collaboration, client computing, and semiconductors. He has 30 years of experience including 15 years of executive experience at high tech companies (NCR, AT&T, Compaq, now HP, and AMD) leading strategy, product management, product marketing, and corporate marketing, including three industry board appointments.