Jan 18 2016
With the rising importance of data-driven decisions in strategic planning, the executives in small, medium, or large businesses are increasingly investing in highly standardized BI systems with uniform data reporting requirements. In 2010, a Forbes Insights survey of over 200 business and IT executives reflected that 61% of survey respondents did not trust the business data they worked with in their daily decision-making processes! This indicates the sorry state of business data as a facilitator for timely business decisions.
Like large organizations, every small or midsize company is also flooded with data about their products, services, competitors, and customers that creates data confusion. Apart from that, every enterprise, large or small, has an established supply-chain framework that churns out millions of transactional data resulting in data overload. Moreover, individual data silos present in hard drives or departmental servers further contribute to the data disconnect between different parts of the enterprise.
The data confusion, overload, and disconnect—just described in the previous paragraph—usually prevent the organizations from making the best and timely decisions when they need those decisions the most. Most of the data buried in individual data silos have been found to serve short-term needs, but they ultimately bring down the enterprise decision-making process to a screeching halt.
When critical data has not been transformed into easily accessible information, insights, or actionable intelligence, what good can that data do to a business? Moreover massive loads of data that have not been cleaned, standardized, organized, formatted, and preserved in usable formats, can never be of any use during critical decision making process.
The foundation principle of data visibility can benefit enterprises in 2016 as follows:
The above three benefits, on their turn, will automatically benefit the strategic planning of a business in 2016.
For every business to thrive and prosper in 2016, it is imperative that individual goals of business functions be aligned with corporate goals. If that does not happen, many disconnected goals and strategies will create a lot of noise and conflict, and ultimately hinder business growth. Moreover, it is not enough to have corporate and departmental goals aligned; it is also equally important to have the goals shared and preserved in a central repository for anyone to view at any point of time. Typically, different departments may be developing performance metrics targeted to their own departmental goals, leading to inconsistent reporting parameters and non-aggregated data for the business leaders or top management. Thus, in times of need, the top management will waste time determining which common goals or strategies have been shared between the different departments. The centralized data visibility model forces departments and individual parts of an enterprise to adhere to standard data-reporting formats and data conformance.
This is where a truly integrated BI system comes into play for ensuring data reporting conformance across the business. If the top executives conduct a health-check of their existing BI infrastructure, they can easily find out if data can be easily shared and exchanged between functions, whether all critical data can be accessed from anywhere in real time, or whether the critical data is available in standard format. Unfortunately, without these characteristics, even the most data-rich organization will fail to deliver results.
The above data-management framework can ensure that standard performance data is available across the business at any time.
The idea of a highly centralized and visible data-management system not only reduces the time and cost required to manage the data, but also removes a compliance nightmare that might exist due to non-standard data troves present in different parts of the business.
In order to provide for a seamless, data-driven, enterprise decision making system, business leaders need to review the results found during gap analysis and plan on allocating money and resources on building a centralized data reporting system that adheres to uniform data-reporting standards, even if not a “one size fits all” data-management philosophy across the business.
A central data-reporting system:
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