We have more data available for analysis than ever before. Data is growing exponentially, in volume and in type. In fact, unstructured data growth – heavy in text, video and images in various formats – has surpassed growth of structured data that previously fit neatly into the rows and columns of relational databases and spreadsheets.
More stakeholders understand the value of insights gleaned from both structured and unstructured data. They’ve seen how analytics can help an organization better serve customers, sell products, develop solutions, cut costs and achieve any number of business objectives.
Though organizational data has earned its rightful place in the spotlight as a key for building a competitive advantage, there are barriers to accessing analysis-ready data. In many cases, IT and business teams are not aligned in terms of their data analysis initiatives. Resources necessary to organize, manage and prepare data for analysis are often constrained. Finally, data is stored in multiple silos that are not integrated, hindering business teams’ access to data for analysis. As such, there’s a growing need for strategies and solutions to blend data for consumption by the various stakeholders that need it.
In his recent webcast, Peter Evans, Senior Integrated Solutions Development Consultant for the Information Management Group at Dell, addresses these issues. Peter discusses the five laws of data integration and how you can break down barriers to perform strategic and effective data analysis:
1) The whole is greater than the sum of its parts: The essence of any end-to-end system within an organization can’t be captured by analyzing the parts independently. As such, integration must include data from every part of the process, even small datasets that seem less important.
2) There is no end-state: Enterprises’ organizational environments continue to change. Data integration plans need to incorporate legacy systems and must also be built to accommodate data coming from upgraded or new systems.
3) There are no universal standards: Or more accurately, there are too many standards, as different types of technologies and processes are generating, aggregating and storing data in specific “standard” forms. Looking at all the data within an end-to-end process is necessary to get a 360 degree view, but that data will need to be profiled and standardized through integration.
4) Information adapts to meet local needs: Data integration works best when data is put into context. It’s the only way to turn data into information. While it might seem redundant in some cases to apply context, a single consistent data model can’t exist without it.
5) All details are relevant: Abstraction is required to solve any problem, and no details can be ignored. Big data projects built on integration will have a huge impact in the coming years. But a strategy to integrate based on legacy and historic data will remain a part of creating new models.
Abiding by the five laws of data integration will set you on the path toward successful data blending and will help ensure quality analytics in your organization. Watch the webcast “Five Ways to Ease the Data Blending Challenge” for other important steps to take. This session summarizes the importance of data profiling and offers a demo on how to use Toad Data Point to automate your analytic workflow.