Why Are Organizations Still Struggling with Their Data?
This is a question I’ve been asking myself for a while. The data infrastructure exists to support Big Data, operational data streams, data quality practices, and the list goes on. Best practices exist for organizations to follow to achieve a strong information management framework and tie data to business processes enabling decision makers the ability to take actions on the insights they’ve gleaned.
This is a question I’ve been asking myself for a while. The data infrastructure exists to support Big Data, operational data streams, data quality practices, and the list goes on. Best practices exist for organizations to follow to achieve a strong information management framework and tie data to business processes enabling decision makers the ability to take actions on the insights they’ve gleaned. A variety of solutions exist in the market place providing BI access to any type of user and that are geared towards a strong IT infrastructure or small business with little to no internal IT support. Additionally, organizations understand the value their data brings to the table. Yet, many companies still struggle with silos of data, lack of visibility, the inability to consolidate information assets and develop the essential correlations between the data they need to drive strategic business value.
Despite all of these facts, the answers are still elusive to me. Sometimes I think that nowadays project sponsors think data management should be easier than it is and cut corners to ensure quick implementation times without weighing the facts surrounding how this will affect time to value. I have seen it many times with organizations that don’t conduct in depth requirements gathering or identify how business and technical requirements are developed to work cohesively together. I have also seen organizations select products based on marketing hype and end up with a subset of the capabilities they require. Within SMBs, there is also a mistake whereby organizations don’t take into account the expertise they require to develop a strong BI initiative and either do not want to invest in the right skill set or feel that the resources currently available can be used without the proper training.
All of these areas contribute to the confusion, but so does the market itself. There is very little that is available in the form of a series of best practices or guide that can be used on a broader level to guide organizations through the transition from traditional BI infrastructures and other traditional models towards agile solutions that help support organizations in this transition. After all, the complexities of data integration haven’t gone away despite the promise of automated processes, more APIs, and easier to use solutions. Luckily, as the market matures, businesses can start to benchmark against those with successful implementations. At the same time, it seems like an integrated approach to data management is still needed across the board to really help organizations support broader data management. Solutions are still piecemeal and full stacks are not always accessible.
Hopefully as the market continues to mature and as more organizations move towards an agile approach to their data management, there will be more success with data management as a whole. But this still requires better planning, knowledge of IT infrastructure options, and an understanding of the value data can bring to the organization if leveraged well.
This post was brought to you by IBM for Midsize Business and opinions are my own. To read more on this topic, visit IBM’s Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.
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