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Guide to Deciding on a Data Integration Method

Cross Point Solutions’ Guide to Deciding on a Data Integration Method

You can look both ways, but you’re probably still going to get hit by data. Operational and transactional information is a requisite facet of running a business today – for several reasons, but it is arguable that it primarily has to with vital decision-making processes about an organization’s future. Business Intelligence (BI) entails a breakdown of company data for making sense of the trajectories and trends, strengths and weaknesses, as well as correlated projecting or budgeting for the fiscal year. Regardless of whether a company is using an OLAP cube or some sort of data warehouse, important company data, such as personnel information, revenue/expense transactions, and inventory, are utilized in Enterprise Resource Planning (ERP) systems and BI tools like reporting tools, budgeting solutions, and dashboard solutions. When deciding which BI device to use, an important question mark arises pretty early in the search process: is it better to integrate to a BI data storage option or to integrate live to and from the ERP system?

From company to company, the answer to this question will be different, related to the specific company size and BI requirement, but transcending any organization’s specific goals, the two methods of integration have their pros and cons. It is imperative, when deciding with BI solution to invest in, to weigh the strengths and weaknesses of each and consider the mode of data access that a product offers as a potentially major deciding factor. Here at Cross Point Solutions we will go about discussing data integration methodology – and how the current options will affect how you create budgets, generate reports, and build dashboards.

First, it might be important to differentiate some terms in the realm of data. Data migration involves moving the information from one source to another, like when shifting data from a legacy hierarchical ERP database to a modern, relational one. Consolidation means to aggregate data from multiple sources into one space.   Finally, integration denotes combining data from various sources into a cohesive, meaningful, accessible view. A data integration tends to involve discovery, preparation, observation, conversion, and transport of data from potentially numerous sources. Data integration is a requirement these days – and beneficial for performance management.

Technology advancement has empowered massive amounts of varied data storage. Integration of all the kinds of company data into one manageable database allows for superior analysis and necessary reporting and budgeting. The more data involved, the more accurate the analytics are for decision-making. Recently, the marketplace learned that analytics can spell life or death for a company.

When applied to the real world, BI solutions utilize dashboards, reports, and budgeting to navigate the ups and downs of the marketplace. 2008’s recession caused large amounts of layoffs, a related spike in the US unemployment rate and bankruptcies. With any luck, this economic catastrophe was a wakeup call for corporate decision-makers, but nevertheless, regular evaluation and response to corporate data is required for navigating the marketplace. Theoretically, integrating data for BI analysis can prepare an organization for a similar crisis by forecasting in such a way that allows for adaptation to the market’s highs or lows before ever entering the storm. Regarding live integration and data storage integration, which will best meet your company needs?

To continue learning more about what to consider when picking the best data integration method for your company, read the rest of this article here.

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