Companies of all sizes are investing in the technologies that can help them mine their greatest asset: data. Investment in analytics is a business investment in the truest sense, but while throwing money at more analysts or analysis software can reap benefits, expenditure on the right tools and assets will yield better outcomes in pure bang-for-your-buck terms.
Businesses with skin in the data game are looking for the best investments to analyze the information at their fingertips in ways that will benefit the organization at large: in daily operational efficiency, in improved marketing intelligence, in strategic insight.
The data analysis marketplace
There are plenty of software platforms out there designed to pull meaningful information from repositories and present it for specific use-cases. The array of specializations is broad as seems feasibly possible, and many products cover several bases: Financial data processing platforms meld into applications that show specific e-commerce trends, which can lend Sales teams information to attune their activities, and so on.
But these types of packages often abstract away from what actually is taking place under the hood. Every single data-focused intelligence package is based on queries to databases.
At the start, analytics emerged as a separate function within IT around a new centralized approach – “the data warehouse.” Analytics has evolved to become more distributed across the organization. Every corner of the business needs analytics – often faster than the data warehouse-centric model can deliver. With very few exceptions, the analytic environment today is complex and varied, resulting in many ways to get to the same answer – regularly with competing results.
Therefore, it is only logical to begin our series of articles with a company that brings business and engineering groups together through the management, sharing, cataloging, and development of the core analytic objects.
We have a long history in this space. There are thousands of users around the globe that use Aginity products for anything from personal data and analytic management, right up to as an enterprise-wide collaborative platform that consolidates the collected knowledge of an organization’s data professionals.
This article covers some of the basics and “wins” available to any sized organization with a degree of investment in its data.
From the grassroots
As businesses become more data-rich, analysts realize fairly quickly that they need a fluid approach to querying databases, one that will adapt to changing business needs.
On a practical level, organizations’ strategies change, so the questions (read “queries”) that need answering change too. Additionally, resources underpinning the organization can change almost as quickly: databases move from on-premise to hybrid, from public cloud to public cloud, from db2 to Amazon Redshift. The table schema themselves change daily, and as the enterprise grows, teams spin up test instances, sandboxed environments, duplicate stacks, and prototypes. Furthermore, people change jobs or move teams and often take key knowledge and context with them.
In short, the playing field changes shape constantly, and the data professional needs Aginity tools as a constant among a changing working environment.
Aginity connects to a broad range of database types from a single point of reference and provides the platform on which team members can work individually and together. As data analysts use the platform more, the role that the user community has played in the platform’s development becomes greatly apparent. Features that were nice-to-haves in the past have been incorporated into the Premium platform, many of which are also available for smaller teams and single users for free in the Pro tier.
For instance, there is context-sensitive tap-to-complete (the platform quietly indexes each database connection in the background), and meta searches for “CustomerID”, for example, will bring up others’ SQL queries and references from all schemas’ instances.
Teamwork makes the dream work
As data practitioners at all levels (granular privilege sets provide some checks and balances for the newbies) can save and reuse each query, the organization builds its library of resources that, over time, will make operations faster and more efficient.
Before long, individuals and teams save enormous amounts of time by reusing, copying, and editing from the shared collection of data artifacts. Single analysts no longer work in isolation, and teams that may be separated geographically and by large time zone differences work collaboratively. As a case in point, Aginity itself is highly distributed across the globe, with teams scattered across Europe and the US working seamlessly together.