by Haley Burton | September 01, 2021

Solving Data Proliferation and Connectivity Challenges for BI & Analytics

Data is the key to digitally transforming your organization. Modern business leaders are chasing the promise of illuminated decision-making models and a deep understanding of their customers. Organizations therefore need to invest in their data strategy to enhance operations performance and build a highly enabled workforce.

Accurate demand forecasting, cost-savings, supply chain mastery, and more are all there for the taking.

But when it comes to data, many companies are facing several data access challenges:

  • Data is often buried and scattered across hundreds of client-based applications and databases.
  • Data is often segregated by source and in formats that aren't analysis-friendly.
  • Providing analysts with actionable data often requires custom-coded integration solutions.
  • Each of these solutions requires maintenance and upkeep against an ever-changing ecosystem of APIs and SaaS endpoints.

Unifying and democratizing your data allows you to acquire timely and actionable insights needed to transform your organization.

The Data Chasm: Challenges in Creating a Data-Driven Organization

Many organizations struggle to provide analysts and data scientists with the level of data access they need to do their job effectively. This lack of access is a chasm standing between most organizations and the opportunities presented by data analytics.

  • Data Fragmentation & Data Silos

    Data is often scattered across hundreds of systems your organization uses on a daily basis. Of course, each system has its own API and database storage unit. This makes getting permission to access each dataset even harder for your scientists and analysts, who are not integration specialists.

  • Non-Standard Data Models

    Each system uses its own data model, with different table rules, tags, lists, and methods for organizing data. Without proper treatment, this data can't be mined for information.

  • Wasted Time Chasing Down Data

    Organizational data is usually scattered across enterprise applications and systems, often under the purview of different company departments. A 2020 survey by Datanami found that the average data scientist spends 45 percent of their time preparing data alone, including the arduous process of gathering from various underlying data sources. The preparation process involves everything from locating and gathering the data to loading and cleaning it. This is a frustrating waste of time for data scientists, who could instead be running more impactful data analysis.

  • Endless Integration Maintenance

    Most enterprise systems aren't static, meaning integrations and maintenance are never just one-and-done setups. Not only do the changes regularly make the data inaccessible, but they can also sometimes change its structure, breaking data flows you've already created.

  • IT Teams are Overwhelmed

    It's no wonder that IT teams are often overwhelmed, and data scientists and analysts are spending too much time on regular integrations - and not even coming close to keeping pace with the demand. That's the main reason why, according to a 2021 survey, only 24 percent of companies believed they are a data-driven enterprise, despite a decade of focus and investment in data-driven digital transformation.

It's time to break through the data chasm. By building a bridge of data connectivity into your IT infrastructure, you can empower your team of data scientists and analysts to generate the insights needed to power decision-making.

The Solution: Building the Connected Enterprise to Empower Analysts & Data Scientists

To unleash the full power of data in your organization, one critical step is creating an enterprise-wide and connected ecosystem of data. Data integration solutions offer your analysts direct access to all the data they need directly from inside their analytics platform of choice - without having to build the analytics integration themselves. Not only can you empower your data scientists and analysts, you can free up your IT team from having to custom-code integrations.

Fortunately, this is all quite achievable for any data-driven organization.

You can start by enabling direct, point-to-point access for data scientists and analysts company-wide. You can also add a centralized data hub, allowing analytics to run on top of a central data warehouse.

Learn more in our guide: 3 integration strategies for data analytics and business intelligence. ‌

Why Choose CData for Business Intelligence Integration Solutions?

At CData, we help aspiring organizations like yours conquer this challenge using standards-based data connectivity solutions for business intelligence and analytics. We understand that every company is unique, so however you want to connect, we'll make it possible. Check out our data management solutions and offerings that can handle any data sources, any application, tool, or system, anywhere at any time, both on-premise and up in the clouds.‌