What do you do if your Sales Operations data and analytics are in disarray?

Prioritise facts over narratives. If you lack the necessary data to do so, hire experts to build the right data plumbing. Remarkably, you don't need a complex data lake model if you have the right talent. Then, ensure your business cadences are integrated into dashboards—any tool will do. These cadences sustain ongoing engagement; without them, dashboards quickly lose relevance after launch. — Ex PivotTable Geek

The problems often come from a few areas: 1. Messy CRM Processes: Inadequate CRM processes can result in incorrect or incomplete data entry, leading to poor quality data that hinders accurate analysis. 2. Unreliable Data Pipelines: The pipeline transferring data from sales operations to the data warehouse is often unreliable, which can result in data loss or errors during transfer. 3. Poor Data Management: The data model and its transformations are not well-managed, leading to inconsistencies and errors in the data set that can skew analytics outcomes. 4. Outdated Reporting Tools: Reporting tools may be out of sync with current data sources or fail to update, resulting in outdated or inaccurate reports.

Alright, so when your Sales Operations data and analytics are all over the place, first, it's like walking into a maze blindfolded. Been there, done that. What's next? Start by gathering your team, hit the ground running. Dive deep into the mess. Identify the root causes like a detective. Once you've got those sorted, prioritize the fixes. Then, roll up your sleeves and start implementing, step by step. Finally, keep a close eye on progress, refine as needed. It's like fixing a leaky boat, patch it up before it sinks!

Identifying the root cause is critical when Sales Operations data and analytics are chaotic. Research reveals that 80% of organizations struggle to manage and analyze data effectively. Pinpointing underlying issues—whether it's data quality, system integration, or lack of expertise—is essential for implementing targeted solutions. By addressing root causes, businesses can streamline processes, improve decision-making, and unlock the true potential of their data, paving the way for enhanced performance and sustainable growth.

Root cause analysis is sometimes obvious and sometimes like playing detective. Symptoms and problems are pretty easy to spot. Solutions aren't even all that hard to dream up - but is most organizations where you enter and inherit a current state (versus building it all yourself), there is a TON of complexity for even the simplest of things. Unpacking years or decades of historical technical debt to identify how to roll out changes without impacting things usually just finds more root causes! There is no perfect path in these situations. In my experience, you are usually in role because the people who owned the old stuff are no longer around. You WILL break stuff, just be ready for it and have a plan. Over communicate to stakeholders.

It is essential to engage relevant teams and departments, such as sales, IT, data analytics, and upper management, to ensure alignment with overall business objectives and priorities. By gathering input from these stakeholders, you can identify the most pressing needs and prioritize solutions that have the greatest buy-in and potential impact across the organization. This collaborative approach fosters ownership and accountability, increasing the likelihood of successful implementation and adoption of the chosen solutions.

Prioritizing solutions is crucial when Sales Operations data and analytics are in disarray. Studies show that 44% of businesses struggle to determine which data to prioritize. By focusing on high-impact issues first, such as data integrity or system inefficiencies, organizations can achieve quick wins and lay a foundation for long-term improvements. Prioritization ensures resources are allocated effectively, accelerating the path to data clarity and enabling informed decision-making for sustainable growth.

Logically, I would prioritize by going after things based on: 1 - Highest impact issues (most bang for buck) 2 - Most time returned to sales people for effective selling (most efficiency gained) 3 - Because my boss tells me to (prioritization by title) 4 - Ease of solution (quick wins) These all have their place and I ordered them in roughly what I think the order should be. Note that I don't have 'what is most interesting to me' as an option although I see that taking place from time to time. There are many ways to try to quantify these things but putting them in order helps set budget for resources, software, timelines for delivery, socialization of complexity of projects, expectation management, etc.

Implementing solutions is vital when Sales Operations data and analytics are in disarray. Research indicates that 73% of companies believe data has the power to transform their business, but only 29% are successful in leveraging it. Taking action on identified issues, whether through improved data management processes or investment in analytics tools, is key to unlocking insights and driving informed decision-making. By executing solutions effectively, businesses can harness the full potential of their data, leading to enhanced performance and competitive advantage in the market.

Socialize a lot during planning but don't let perfect get in the way of progress. You will never get it 100% right - just try for the least worst option and be prepared for unforeseen issues (rollback plans or resources on standby for unplanned work). When you are implementing, please always test, test, test. QA and UAT are needed but for big ticket items, please also try to pilot with a decent sample of actual users prior to a GA launch. This takes more work but it's the best option available when things are complex. Budget time after pilots for implementing new requirements and socialize that dates are outcomes in many of these types of programs.

Whenever I approach these types of "bad data" or "bad process causing bad data" or "bad systems on bad process causing bad data" kind of things - I try to start by measuring the current state 'how bad is it?'. This helps put a benchmark in place so that solutions can be measured to see their impact. For example, if you see something accurate only 50% of the time, an increase to 60% is good progress even if there is still a large issue! These things are incremental and you typically find new problems when chasing known problems. Being able to demonstrate forward progress is helpful when socializing to leadership or giving status updates.

Quality of Analytics output is dependent on the underlying data. First as many here have rightly stated, conduct a Root Cause Analysis. Assess as to whether data issue is due to the quality of data entered or is it a deeper issue related to the data structure or interdependency between data objects. The possible solutions could be as simple (or manual) as data cleansing to possibly a redesign of your crm data structure. To improve your analytical output; you need to start with possible problem statements or hypothesis and then use your analytical tools to substantiate or disprove your initial assumption.