By Juan Nunez, Director of Data & Analytics at Micro Strategies
Business today moves at an extremely fast pace. Decision-makers need access to critical data faster than ever before. Traditional approaches do not support fast and iterative data exploration, limiting your ability to get timely insight from data. A new way of thinking and accessing data is needed; one that matches the availability of insights to business decision making demands.
Data warehouse, data lake, 360 view of the customer, decision support systems; all of these more traditional approaches to data call for long-term integration projects that often fail to deliver or maintain results. With the increasing complexity of data landscapes, organizations need fast and reliable ways to gain insights from data. Business environments move with increasing speed and demand timely decisions made on the merits of data and thorough analysis. A different approach is needed to enable organizations to access just in time data, properly prepared and managed.
Fast Data Insights is an architectural approach predicated on accessing data, regardless of the source and only moves data when required. It is a process by which data sourced from various systems can be linked, cleansed and analyzed without the need for costly IT development cycles. It is a set of quick start services, carefully curated tools, and applied intellectual property gained through years of data integration, data visualization, and analytics enablement.
Fast Data Insights does not replace the need for a data warehouse or a mature process to handle a company’s most precious resource. Rather it is a critical addition to an organization’s data management architecture. It is not a silver bullet to handle data complexities, but an accelerator to detect and leverage the value within disparate data assets.
Decision makers cannot wait for integration projects to be completed before analyzing and gaining insight from their data. In reality, they are not waiting. They are most likely leveraging talented analytics resources to gather, cleanse and analyze data in less than ideal circumstances. These resources are spending a large amount of time finding sources, gaining access, moving data to available tools (spreadsheets, Access, text files, etc.). The work is getting done, but is that a best use of analytics resources time?
Business teams worry about asking the wrong question as it might trigger an eight-week integration project, only to find out at its completion the data does not support the answers expected and there is a need for additional details.
With Fast Data Insights, business teams quickly virtualize data sources, link separate systems and start answering questions immediately. Whether data is housed on websites, databases, PDF fi les or other places, these sources can be accessed without penalty as needs evolve. Some sources of data are immediately known while others are identifi ed in time. This is to be expected as analysts continue to dig deeper, and require fl exibility to add sources, and immediately start to work with them. Once valuable insights are identifi ed they can be integrated into regular business processes; however, the Fast Data Insights approach allows organizations to start leveraging these in analytics and visualization processes immediately.
Fast Data Insights combines an architectural approach at data virtualization with a set of over 250 out of the box connectors. A centralized interface to prepare data that learns the common cleansing steps taken, recommending how to address new sources based on what analysts have done in the past. There are hundreds of ways to consume it, through easy connectors to visualization or analytics tools. Additionally, there are quick start services to create a data on demand environment, something like Netflix for Data.
While the concept of data virtualization also known as data federation has been around for over 20 years, it failed to deliver on the original objective of interacting with data regardless of where it was located. With the advancements in workload distribution, the introduction of containers and appropriate application of best practices, organizations can gain valuable data insights by applying this reemerging architectural approach.
To prevent business opportunities from slipping through their fingers, organizations need to match the availability of insights from a changing data landscape with the pace of decision making.
