Best Practices: Improving Data Preparation for Business Intelligence

You’ve probably read it a thousand times already, but we’ll repeat it, ‘data is the new lifeline’ of companies today. Unimaginable volumes of data and the number of channels through which this data is flowing is overwhelming systems and resources. However, companies have yet to make use of this data. Less than 0.5% of data is ever analyzed and prepared for business intelligence.  

Most of our data is not being captured, prepared, and accessed in a way that should make it easy for business users to derive insights from. Most organizations are still so busy fixing and making sense of their data that they get little time for truly understanding and using this data for business intelligence.  

Why Organizations Are Still Struggling With Insights and Business Intelligence

Why are organizations still unable to make sense of data despite the rise in data preparation tools and solutions?  

Some key reasons are:  

• The Lack of a Definitive Data-Driven Culture

  Although companies say they are data-driven, they are still far from achieving that goal. The data mindset is still pretty much limited to executive leadership. At the staff level, there is limited knowledge of data and its impact on the organization. For instance, a company’s sales rep is not equipped with data entry standards. Similarly, the marketing department is struggling with incomplete, invalid data in the CRM. Despite using advanced CRMs like Salesforce and HubSpot, employees are still struggling to make sense of their data.  

• The Lack of Focus on Data Quality Issues

  Companies are seldom aware of the underlying data quality issues with their data. Most have no data quality framework in place. Data is gathered and dumped in data lakes, never to see the light of day until the company has an urgent need – like consolidating customer records for analysis. It is only at the time of need do they realize they have data that is corrupt and useless. For instance, while a marketing manager may think the department has secured 1,000 leads after a campaign, the reality is, only 7,000 of the total leads have usable data.  

• The Lack of Right Tools and Solutions

  Another common reason for failing business intelligence initiatives is the wrong investment in tools and solutions. Companies willingly spend millions of dollars on advanced systems and cloud migrations while ignoring their data quality. Eventually, these tools are unable to serve the purpose for which they were acquired. For instance, many companies try to move to the cloud even though they don’t really require it. Not only do they end up with ballooning costs but worse with systems that have little impact on service quality.  

• The Lack of Business Support

  Data-driven projects has largely been an IT responsibility. When an organization discusses data quality, it only pulls in the IT team in the room. Business users who are at the frontline of handling bad data consequences are hardly ever involved in the discussion unless they are from the leadership team. This exclusion and lack of support for business users make it difficult to acquire any form of valuable insight or to execute any effective program for business intelligence.  

• The Lack of Right Human Resources

  Companies gather data, but they don’t have the right talent to make sense of this data. For instance, data analysts, who are supposed to analyze data and derive key insights for business intelligence spend over 80% of their time in fixing data quality issues. This is not just a waste of talent but also time, effort, and money.  

As easy as it sounds, business intelligence isn’t something you can initiate just because you have more data. You need to spend a significant amount of time preparing that data and implement data quality workflows to get the data you need for insights and analytics.  

Even if you’re using a top-in-line data preparation software, you’ll still need to implement business processes to support tools and solutions. It’s important to understand that tools are only as smart and capable as the person using it. If the above described problems are not resolved, you’ll find it difficult to prepare and use your data for its intended purpose.  

Best Practices in Data Preparation for Business Intelligence  

Whether you’re a business user or an IT user, you need to take data quality as a serious matter. When it comes to business intelligence, it’s imperative to understand that mistakes can have drastic ripple effects. For instance, it’s not uncommon to see companies use last year’s market share data to make this year’s business decisions – including sales and promotions.  

In 2020, you can no longer rely on outdated methods. You have to let your data tell the real story.  

So, if you’re preparing data for use in business intelligence to scale business operations or to understand your customers better, it’s imperative to have the best data preparation practices in place. Some of these are:  

1. Assessing Your Data:
   Before you start with any data-driven initiative, try profiling and assessing your data for any underlying issues. For instance, it’s easy to spot blatant typos and misspelled names, but it’s not easy to non-printable characters, or characters that creep into different fields. For that, you’ll need a profiling feature that can allow you to scan your data sets and get visibility on the kind of problems affecting your data. Once you profile your data, you will have a clear idea of the underlying problems and can make informed fixes to your issues.

2. Making Data Cleaning a Priority:

   Any time you use data for any business purpose, make it a priority to clean your data and by cleaning, it’s not just fixing typos or bad addresses. It means checking for duplicates, removing obsolete or outdated information, replacing incomplete information with complete information, verifying and validating records, etc. While you can do all the textual fixes on Excel, you cannot deduplicate or standardize data on Excel easily. For that, you will need to use a data preparation tool.

3. Set Up an Automated Data Cleaning Schedule:

   Data is constantly streaming in through multiple channels and you don’t know when you would need this data to make key business decisions. For example, you’d want data from the last three months to run a promotional campaign in time for 4th July. But instead of executing the campaign well in time, you had to spend 2 weeks in preparing the data. That’s a lot of wasted time. So it’s always good to schedule a monthly or quarterly data cleaning routine, depending on the inflow of data. This way, you have data ready, whenever you want to use it for its intended purpose. 

4. Make Use of a Data Preparation Tool:

   Most data prep tools today are advanced and easy to use. They will help you prepare your data by walking you through a data profiling, cleansing, and deduplicating process.

5. Treat Your Data Like it’s a Lifeline:

   Back to our intro – your company’s data is the new lifeline. Want smooth operations? Satisfied customers? Efficient processes? Take care of your data. It affects every aspect of your organization.  

To Conclude

Business intelligence, AI, ML are all digital technologies driving companies into the future, but they cannot happen unless companies resolve existing problems – like data quality issues, human resource issues, business process issues, and so on. To truly benefit from BI, you need to understand your data, have the right data prep tools, and implement the right processes.  

DepositPhotos – data

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