6 questions to help you fill in the holes and get meaningful insights
We hear you. You want data that will give you insights into who your customers are, what they want, and how you can meet their needs. But what if your company hasn’t quite mastered the art of analytics – or it’s facing one of the “Big 3” data hurdles that lead to inaccurate assumptions and insights?
Getting on the path to meaningful business and customer insights relies on one critical factor: good data. To determine whether or not your data is accurate, complete, and genuinely usable, ask yourself these six questions:
- Is your data complete and usable?
- Is your data accessible and timely?
- Are the right controls in place to audit the data?
- Are your data processes repeatable and consistent?
- Is your sensitive data secure?
- How quickly does your data decay (and is it being refreshed frequently enough)?
Is your data complete and usable?
Data usability issues can come from any one of seven scenarios:
- Your data isn’t standardized. The customer’s province is recorded in three different ways in three different systems: ON, Ontario and ONT. Territory management gets challenging if you need to clean and compile information each time you do analytics.
- Your data is ambiguous. When you say “blue pen,” is the pen itself blue or is it the ink? The same ambiguity exists with customer data. When you’re recording results from a customer interaction, how do you specify if it was positive or negative in a consistent way? Assigning a quantitative data point makes it easier to analyze customer satisfaction.
- Different elements of customer data aren’t linked. You have invoices in one system, personal information in another, sales funnel stats in a third. When information for a single customer exists across systems, you can’t get a single view, which leads to bottlenecks in your sales process.
- You don’t understand where the data comes from or which sources are the most accurate. Which system has the most accurate customer address – the CRM, ERP or web?
- You don’t have the right process in place. Just because data has always been stored or used a certain way doesn’t mean you should keep doing it that way. Reevaluate processes to ensure you’re using ones that make sense.
- You’re suffering from data transformation issues. Data accuracy can easily be compromised as it flows from the CRM to downstream processes. Without quality checks at each stage, it can be inaccurate in summary reports, quarterly planning and territory planning, leading to poor insights.
- You don’t have a process for maintaining historical data. When people change jobs, departments evolve and priorities change, data can be lost. If you’re looking for sales trends from the last three years to forecast for next year, but your company just went through a reorg, you may be out of luck.
So what SHOULD you do to prevent those scenarios – or get past them?
- Define the data you need. Start by modeling your data around your business needs and work backwards. Determine what you need to grow sales. It won’t all be useful for your purposes, so be selective. This will help you build a strong data foundation.
- Build a process for collecting correct data. Decide what data you want to work with. Take a cross section to identify inconsistencies, then identify areas where you can create value. Get analytics experts to weigh in with process recommendations to ensure completeness and integrity. One strategy is to have data stewards be accountable for data quality, as long as they roll up to a central point of accountability.
Is your data accessible and timely?
You want the right data, where and when you (and your sales force) need it. But as you know, it’s not always that easy. You probably have customer information in multiple places – accounting, operations, even social media.
Suppose a salesperson needs to correlate a CRM customer profile with purchasing patterns, profitability, payment history, and value as a brand ambassador on social media. Someone would have to manually pull that data from all the different systems and merge it with the CRM. That could take days, and create a bottleneck in the sales process.
What SHOULD happen? To get on-demand access to data, the customer data spread throughout your enterprise should be integrated into a self-service model that’s fully accessible to your sales team. With the information at their fingertips, it’s easier to win a competitive quote, access vital information during a customer meeting, sell your product at the right price, or identify an untapped market.
Are the right controls in place to audit the data?
Auditability is ensuring that your data can be proven – that it has data lineage. Your executive team needs proof that any customer analysis is backed by solid data. But if you’re still manually moving numbers and other data from one system to another, consistency and accuracy are compromised.
Manual processes also make it hard to backtrack consistently; it can take hours to validate a summary a second time. It’s a costly process without a lot of value; you’re using hours and hours of resources validating data instead of analyzing and actioning insights.
What SHOULD happen? Put an automatic auditing system in place. That way both data and the data trail are always controlled. It’s systematic and repeatable, creating consistency that builds data trust and accuracy.
Is your data repeatable and consistent?
As soon as a human being has to “process” data, it opens the door for error. Plus, you’re limited by resource gaps during vacations, sick days, and during other project priorities. If a manager has to audit data from the analyst to confirm accuracy, it’s not timely, accurate or reliable.
When people intervene in the data process, trust in the data breaks down. Sometimes that tampering is intentional, like when a director changes the numbers based on his or her own reasoning, and sometimes it’s not. But it’s always damaging.
So what SHOULD happen? Automation. It’s the only way to ensure consistency in how data is processed.
Is your sensitive data secure?
Is sensitive customer information floating around on people’s laptops in spreadsheets, emails, etc.? Are people accessing data that can create conflicts of interest or introduce corporate risk?
Governments are introducing more onerous security requirements around data – the General Data Protection Regulation (GDPR) on data protection and privacy for all individuals within the European Union is just one example. Violating those regulations can mean steep financial penalties. Plus, sensitive data that gets into the wrong hands can lead to significant risks to the company.
What SHOULD happen? Data security should be a major concern for organizations. The right technology and processes will secure your data while still allowing people to access data quickly to do their jobs. Put a robust security model in place – the right solution will suppress data based on people’s roles, without preventing access for other users who need to access sensitive data within the same data set.
How quickly does your data decay (and is it being refreshed frequently enough)?
Data has a finite lifetime. If it isn’t refreshed often, it gets old – addresses, phone numbers and titles change all the time. Not to mention even more vital customer intelligence such as product usage and recent payment history. If your data isn’t fresh, then there’s a good chance it’s wrong – and that means it’s not doing anyone any good.
What SHOULD happen? Determine how often data needs to be refreshed to keep it in the window of usability. And if there is data redundancy within your organization, it’s also important to determine what data you can trust more over other data.
Quality is more important than quantity
If you don’t know where to start, remember this: Big Data doesn’t necessarily mean you have to have a lot of data. You can get big results from small amounts of information, as long as it’s the right information, and it’s collected in a way you can trust.
So start small. Set up processes for pulling, cleansing, standardizing, securing and linking your data. Get experienced guidance on what best-in-breed tools you need to run, manage and make use of your data for sales insights.
And remember that you don’t have to run your program internally. An external managed services analytics system can be a good way to ramp up fast without a major upfront capital investment. Gensquared provides a quick initial data discovery and assessment – we can help sales leaders like you understand how to plug the holes in your customer data, and get meaningful insights for sales growth.