Data analytics' impact on decision-making, product experimentation, and the user experience is undeniable. But the process of not only getting data in, but also getting insights out, has been too hard for too long, and it’s hindering organisations’ ability to successfully innovate.

From implementation issues to poor communication with stakeholders, businesses face challenges at every turn. Thankfully, there are also solutions to mitigate these. Let’s break down the seven deadly sins organisations risk committing along their analytics journey, and how to overcome them.

Complicating analytics from the get-go

A sluggish setup is the first sin. Before a business can input its data, it needs to build a tracking plan, which gets sent to an engineer. For each event, the engineer needs to write specific code; more complex events require more engineering support and code. Suddenly, the business has created unnecessary complications before insights have even been collected.

The solution is simply one line of code. A good product can limit the need for more code for complex tasks by offering low and no code functions, freeing up engineers’ time. This approach also allows teams to automatically populate baseline metrics and dashboards, and get going faster. But it’s not all about data quantity; quality is equally important.

Overlooking the importance of data governance

Incomplete, unstructured, or siloed data can lead to poor insights - and the resulting chain reaction can have serious knock-on effects for the wider business. However, when analysts do try to clean and organise data, they often end up spending too much time on this, and less time gathering the insights that truly matter.

To overcome this, prioritise data governance. When done right, it can streamline and strengthen any data strategy. For instance, analysts can help develop a framework that acts as a guide. This can include a “data dictionary”, which is an organised list of data that allows users to learn about behavioural events, and contextualise where customers are in a product.

Quizzes can also be used to test employees’ knowledge of good and poor governance practices. And all of this can play a significant role in ensuring good data quality, and mitigating concerns around misuse.

Not respecting analysts’ time and workloads

Data teams are inundated with requests. Juggling these can be overwhelming and stressful, particularly when every requester believes their analysis is the highest priority. And if analysts are constantly working at maximum capacity, with little time for other tasks, it can impact a business’ entire data strategy.

The key is to set expectations. Analysts can work with stakeholders to understand which requests are truly urgent. They can also make analysis backlogs visible to the wider business, so that other functions can see which tasks are in-progress, and where there is capacity for requests.

These actions allow data teams to better manage their workloads and ensure their work is creating the most possible value. But the work shouldn’t just fall to those with the technical know-how; it’s equally important that non-technical teams can easily self-serve.

Failing to accommodate for non-technical teams

Businesses need to support every team. Analysts may understand what questions to ask, what charts to build, and how to run analyses, but non-technical teams don’t. And if they don’t have the right resources and support, they’ll fail to uncover valuable insights that can help drive the organisation forward.

The solution? Leverage a low or no code platform that can democratise analytics. That way, non-technical teams can easily access, and use, analytics functions to get to their critical insights faster. For instance, marketers can get insights about what customers are doing across channels, campaigns, and conversions, without needing any technical skills. Yet, business outcomes will be limited if users don’t know how to get the answers they need.

Not leveraging generative AI

Another sin is not using technology to answer questions and develop understanding. Say a non-technical employee is self-serving, but they’re struggling to understand what an event is, and why it matters. Naturally, their first port of call is quizzing an analyst - but it shouldn’t be. There’s another way for them to independently source an answer.

The solution is Generative AI. By training a model on relevant data, content, and definitions, non-technical users can ask questions, and receive answers, in simple natural language. A model could also visualise its responses, and suggest recommendations for further questions.

If a user asks AI to define an event, the model can do so, and then suggest a follow-up question around important events for the user’s function. This approach doesn’t just develop a non-technical employee’s knowledge - it boosts their confidence when leveraging analytics.

Poor visibility into the user’s journey

The sin? Gaining insights, but not allowing teams to actually insert themselves into a user’s experience. A lack of visibility here could stop a business from identifying bottlenecks in the onboarding process, or understanding the customer journey within a product.

To overcome this, consider cohort analysis. Users can be grouped based on a shared characteristic, such as those who failed to checkout. Then, the business can drill into each user’s path and interactions with the product to understand what went wrong. By doing so, a company can diagnose and rectify issues, uncover user insights, and experiment with product features to elevate the customer experience.

Failing to keep stakeholders updated

Not keeping stakeholders in the loop is a crucial mistake. Poor communication could seriously impact executives’ perception of how valuable analytics actually is. And for those who do keep leaders informed, there’s the additional challenge of communicating successes in a way that they understand - succinctly, and without jargon.

The solution? Masterful storytelling. This process is similar to writing a book; stakeholders need a narrative with a problem, solution, characters, and the hero that is the data. All of this allows leaders to visualise exactly how analytics is shaping business outcomes. And this can be a core driver in securing further investment.

Analytics doesn’t need to be complicated. By tailoring support for every single function, leveraging the right tools, and keeping executives updated, businesses can drive product innovation, growth and success.

Adam Greco is Product Evangelist at Amplitude