Given the overwhelming importance of data to organizations, anything they can do to speed up troubleshooting problems in the databases they use is of great value. If a company can speed up or avoid troubleshooting, it frees up time they can invest in doing and building more with their data. Careful attention to database query construction and execution also pays similar dividends, as improved database performance helps organizations get more done faster.

I work as Director at ThoughtWorks in the database and DevOps space. I’ve been here for 20+ years and I vaguely remember my first project at ThoughtWorks in 1999 when we had just started using Agile software development practices. The basic challenge we faced was how to move database changes at the same pace as application code and keep them in sync so that deployments would work. At the time, we had to invent all the tools, processes, and techniques that we needed.

Welcome back to our five-part series in which we discuss a top habit for DBAs to increase business functionality. In part one of this series, we discussed the importance of a capability maturity model (CMM) and more specifically Level 1 of the information management maturity model (IMMM) and how the framework can provide a step-by-step process for DBAs to follow while also allowing businesses to gain skills along the way.

Data provides an essential basis for reports and analytics, with the databases storing the data now driving and informing most custom and line-of-business applications. Thus, anything organizations can do to speed up troubleshooting for database problems is pure gold. In fact, time saved on troubleshooting turns into time organizations can invest in being more productive and profitable.

Indexes play a critical role in SQL Server query performance. SQL Sentry Fragmentation Manager helps you make intelligent decisions about index management based on the table and index information collected. In this blog post, I’m going to discuss index fragmentation, several ways you can identify it, and why a more granular approach to index maintenance can save time and allow you to focus on other tasks during maintenance windows.

Organizations of all sizes invest in IT monitoring and analysis tools. But just because a computer knows what’s wrong doesn’t mean it can communicate those details effectively to IT teams. There is both art and science behind communication, which is why IT teams heavily rely upon data visualization. As organizations scale, so too does the importance of data visualization. Similarly, automating data analysis becomes increasingly useful with scale.

Digital transformation is creating many challenges for businesses including data platform diversity, IT skills gaps, cost control (especially cloud) and a more complex technical environment that can have performance issues with many possible causes. Speed, always-on, outstanding customer experience, and cost control are the business requirements driving digital transformations and resulting in performance and risk challenges facing database operations teams. What are those challenges?

In software development, processing and storing data in different states reflects the business rules an application is built on. The heart and soul of any software application is the data that is persisted in databases for retrieval and further processing. The database system (SQL or Non-SQL) chosen for an application must serve the required data processing and storage needs of the application.

The best business decisions are backed by data. Companies that constantly collect, analyze, and proficiently store this are more likely to succeed in the long run. An organization's database can be a direct source of revenue. Not only in the sense that data can be sold, but the insights produced can help the business too. Data about customer habits, market trends, etc., can enable a company to optimize its practices and find the most effective way to conduct tasks.