What makes BI meaningful?
Business Intelligence (BI) is a broad domain and is used to
accomplish a variety of goals. Whether we’re talking about predictive analytics
or lagging KPIs, the common thread is data visualization that drives effective
The trick is recognizing the difference between a report that has a large quantity of data versus one that has the right information.
Standardized reporting is an intricate type of reporting process that aims to produce consistent, reliable, actionable information from disparate systems or sources. A reporting process is standardized if it can be applied across different business units or sub-units in an organization. The processes that generate and collect the data to be reported on must remain the same across all the business units.
For an organization to understand the status of conditions in real time, and make decisions quickly, standardized reporting is required. A universal understanding of information enables clarity and transparency. Clarity supports effective communication based on trust. And studies show that effective communication leads to enhanced productivity and deeper customer relationships (Source).
It’s not a stretch to say that data consistency creates a competitive advantage over other organizations that do not have standardized reporting processes.
Let’s say you’re planning to repaint your living room this weekend. You shop for supplies online: drop cloths, painter’s tape, and brushes. Alexa, innocently, puts scrub brushes in your cart instead of paint brushes. The delivery arrives the next day. Do you have the tools you need to get the job done? The scrub brush has a handle and bristles! The bristles can be dipped into the paint! Will it require more work to create the desired outcome with a scrub brush rather than a paint brush? My guess is, no matter what, it’s not going to be pretty.
It’s not that the scrub brush is a bad product. On the contrary,
it’s probably the highest rated scrub brush on the web. The issue is that you
need a paint brush specifically designed to apply paint evenly and precisely.
You need the right tool for the job.
Flash forward to the work week: How often are we using the
wrong tool for a job?
In previous posts, we established that data needs to be clean in order for organizations to make sound decisions, and we introduced a 5-part framework for sustaining data quality.
Once an organization has determined the critical pieces of data, and established a governance process around it, now it is time to improve the quality of data.
Data cleansing is an expensive and time-consuming process.
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Before attempting any clean up, analyze and quantify the quality issues that you are trying to address. For example, are there any fields that are null but should have a value? Are data types enforced (only numbers in an “integer” type field, etc.)? Are values within an expected range? Is there conflicting information?
In a previous post, we’ve established that data needs to be clean in order for organizations to make sound decisions, gain a competitive advantage, and improve the bottom line.
But, before jumping to fix your data issues, it’s important to establish a framework that ensures the data will be usable in the long run—not only immediately after a big cleanse, which is often time consuming and expensive. This 5-part framework provides a comprehensive approach for addressing existing data quality issues, and prevent issues from arising in the future.
It goes without saying that data is critical to make strategic decisions, to run operations, and to perform business functions.
- Healthcare companies derive analytics from clinical and claims data to meet quality measures, improve care, and better manage high-cost and high-risk populations.
- Manufacturing companies rely on performance data to improve efficiency, increase yields, and lower costs.
- Retailers rely on data to predict trends, forecast demand, and optimize pricing.
- Financial services organizations perform advanced data analytics to drive revenue and margins through operational efficiency, risk management, and improved customer intimacy.
All of these scenarios require vast amounts of data. Regardless of industry or company size, nearly every business is relying on gathering and leveraging data. Being a data-driven organization is an absolute necessity to gain a competitive advantage.
IT is uniquely positioned to have access to a comprehensive set of data which is stored on or passes through the company’s infrastructure. IT, therefore, carries a responsibility to provide end users access to this data, and to play a vital role in its effective use.