即使是数据驱动型企业也未能充分发挥其数据的潜能_AI阅读总结 — 包阅AI

A recent survey found organizations that relied on data to guide business decisions during the COVID-19 pandemic emerged stronger and better positioned to navigate the market volatility. Investments in data pay off — or have the potential to.

The path from data investment to desired outcomes is not linear. The same survey also found that while most organizations have increased investments in data and analytics, less than half give themselves high marks for their ability to extract valuable business insights from that data. This statistic resonates with me because, in my work in the data analytics and connectivity spaces, I’ve repeatedly seen organizations attempting to implement data-driven decisions while swimming upstream due to inefficient tooling.

Many factors make up a successful data strategy, and best practices vary depending on your industry and goals. I don’t pretend to be an expert in developing a comprehensive enterprise data strategy. However, a key — and often overlooked — piece of the puzzle is proactively identifying the best tools to extract the business insights that will power your desired outcomes.

So, how do companies close the gap between their enterprise data’s potential and the ability to access and use it seamlessly?

Most Organizations Need More Agility for Proactive Decision-Making

Data fragmentation is a widespread challenge that makes executing a proactive data strategy nearly impossible. When critical business information resides in siloed and disparate systems, retrieving data, converting it to the correct format, and extracting actionable insights becomes too time- and resource-intensive. Despite leaders’ best intentions to lean on data during decision-making processes, many fall back on gut instinct when data extraction proves difficult.

Governance adds another layer of complexity to data management efforts, often leaving enterprises needing help to achieve agility and security simultaneously. Because it’s so difficult to establish a comfortable middle ground, enterprises frequently end up at one of two extremes:

• Data access is completely locked down. Strict access controls prevent business users from quickly viewing the data required for timely decision-making, stifling innovation and responsiveness to market changes and customer needs. While intended to safeguard sensitive information, stringent security measures inadvertently create information bottlenecks. In response, users turn to IT for help, creating a culture of over-reliance on IT employees for analytics support or developing workarounds that entirely cut data out of the equation.
• Data access is entirely open. Users can view data unrelated to their business unit and share data in unstructured ways. As a result, companies need to understand who has access to what information and how data is disseminated throughout the entire organization, which invites security breaches and compliance issues. Additionally, the absence of robust controls and oversight compromises the integrity of the data itself. Over time, this gap erodes trust and confidence in the information used for strategic decision-making.

The best way to solve the data access problem is with tools designed to support visibility and security simultaneously. Finding the right solution enables the rest of your data strategy to fall into place organically and repeatably.

Four considerations to identify data connectivity tools primed for agile decision-making

No matter what data connectivity solution you choose, the investment should bolster security and governance measures without introducing unnecessary friction that prevents users from accessing the information they need to do their jobs.

When considering adding connectivity capabilities to your existing infrastructure, evaluate a tool’s ability to:

1. Centralize Connectivity and Governance

One way to solve data fragmentation and security concerns is with virtualization solutions that provide an access layer to data from siloed locations, creating a central hub. Traditional data integration, or ETL, processes are often helpful in enabling historical data analysis and moving massive amounts of data into a central repository. However, building governance layers on top of duplicated data can be challenging, and stakeholders often need help getting the data they need for timely analysis.

Virtualized data, on the other hand, remains at its source. This means that live data from across your technology stack is connected in a unified platform without replicating or storing it in a database or data warehouse. Plus, data virtualization offers the added benefit of supporting user governance by keeping an audit trail of who has accessed what information and enabling you to set user access controls by job level and function.

2. Support Users of All Technical Backgrounds

A common mistake I see enterprises make when executing data-driven strategies is selecting connectivity tools that don’t support end users. Line-of-business employees must access and understand their data to make accurate, complete decisions and grow their businesses. Often, data tools cater only to IT, forcing stakeholders to request data through their IT team or create workarounds that are usually insecure, noncompliant, and less productive.

Before onboarding a product, consider all the ways users throughout your organization need to connect to data — from analysts using spreadsheets to marketing creating targeted campaigns — and ensure your choice truly supports their individualized job functions. Similarly, evaluate whether your selected technology balances your most technical organizational needs with your least technical users so that employees across departments can find value in new tools without needing IT support for every action and insight.

3. Work With Your Existing Technology Stack

Many enterprises have elected to move data to the cloud to capitalize on its scalability, cost-effectiveness, and collaborative capabilities. Other organizations, particularly those in the healthcare and financial sectors, are bound by regulatory requirements to keep data on-premises. No one-size-fits-all connectivity tool works for every scenario, so think through your unique infrastructure setup, business objectives, and compliance regulations as you look to fine-tune and execute your data strategy. Data connectivity tools should enhance your existing technology stack, not undermine it.

4. Future-Proof Existing Strategies

No matter where you are in your data journey, you likely have some degree of data management practices already in place. However, even robust and valuable data management solutions sometimes begin to show their age, and connecting them to modern data ecosystems can prove challenging. If you’re in this situation, look for a solution to layer connectivity drivers on top of your existing platform to enhance compatibility with modern data ecosystems. This approach allows you to future-proof your current infrastructure without losing past data management, governance, and security investments.

Behind Every Data-Driven Decision Is Seamless Connectivity

Leaders can all agree that improving data-driven decision-making is critical today — it’s the execution that trips companies up.

Before you invest significant time and resources in analytics capabilities, take a step back. First, it’s critical to ensure your organization’s data infrastructure enables all users across the enterprise to securely access data whenever and wherever they need it. No matter how robust or well-intentioned your data decision-making strategy is, it takes seamless and secure data connectivity to hit your KPIs.

Are you making the right technology investments to live up to the potential of your enterprise data?