Data Management

Model-Based Systems Engineering: Value of Moving to Data-Centric Solutions

Across industries, more than 85% of enterprises are undertaking initiatives to improve their model-based systems engineering capabilities (MBSE) — especially in aerospace and defense.

From Dirty Data to Actionable Insights: The Impact of Data Cleaning in BI

The volume and frequency of cyberattacks targeting government resources and critical infrastructure continue to escalate at an alarming rate.

Choosing The Right Data Governance Framework

Implementing the right framework is key to optimizing data value while mitigating risks, but there are several different data governance frameworks you can choose. You need to ensure the one you pick aligns with your organizational goals.

Data Strategy for Building Sustainable Revenue Growth

Many times, the gaps in revenue are not a function of the sales teams.

We find that it’s often a function of the process and data they have to go to market for acquiring, retaining, and expanding customers. That’s why we believe a data-driven revenue growth strategy is essential.

How to Build Data Trust Through Effective Governance

If you can’t trust the data you’re looking at, how valuable is the data? Unreliable, low-quality, or inaccurate data simply leads to poor analysis and decision-making. This can quickly erode user confidence despite organizational efforts to use data effectively.

Expert Strategies: Successful Power BI Adoption for Better Data Analytics

Microsoft’s Power BI makes manipulating and analyzing the massive amount of data to drive business insights manageable. By applying powerful, advanced analytics to business challenges, users can extract information for better data-driven decisions.

Data Warehouse vs Data Lake Pros and Cons? The Differences Explained

With critical business decisions relying on a large amount of data, the decision to use an enterprise data warehouse strategy or deploy data lake stores is a significant one.