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Self-service analytics, put your data to work


There's a lot of buzz around the idea of self-service analytics, and it's easy to see why. The democratization of data has been talked about for years in marketing and tech circles, but this year it seems like everyone is talking about it. But what does "self-service analytics" really mean? And how can you start using your data effectively in your business?

Self-service analytics and the democratization of data have become heavyweight buzzwords in marketing and tech circles this year. In a recent HBR research, 86% of company executives stated that their company's employees required better technology to make data-driven decisions, and they identified self-service analytics as one of the top technologies they will embrace in 2022 to achieve this goal.

Self-service analytics and data democratization are two sides of the same coin. Both revolve around making sure everyone in your business has access to more data that can help them do their jobs better. Data democratization is about improving data literacy among everyone in your organization, so they can use that information more effectively without needing any special training or skills. Self-service analytics is about empowering everyone in your organization with tools so they can access data themselves, rather than having someone else do it for them (or paying someone else). 

Data literacy is a problem in most organizations.

Data literacy is the ability to effectively interpret and use information from multiple sources (such as text, images, and videos) using appropriate tools (like spreadsheets). It's about being able to make sense of complex information so that it can help your business make better decisions.

If you're not sure what "data" means or didn't get a chance to learn more about it during school—or if all this talk about cookies makes you feel sick—here's an easy way for us both: Think about something familiar like pizza! Pizza has lots of toppings such as pepperoni or cheese; some come on top, others underneath depending on where they are made; there may be different sizes available depending on how many people want pizza today...the possibilities are endless! And if those aren't enough variables already then maybe we should consider adding another variable like whether someone ordered extra sauce too? Now imagine trying all these combinations out together simultaneously because remember our goal here isn't just finding out who loves pepperoni but also finding out which combination works best together given any given situation."

Data literacy is a problem in most organizations. Many marketers are struggling to understand metrics, apply them correctly, and get the most from their data. This finding probably won't surprise many leaders, who know that their teams need training on how to use analytics effectively. But this isn't enough to fix the issue – you also have to be transparent about how you're using data — and why — so that your employees feel comfortable sharing it or asking questions if they don't understand something.

If you want them (and yourself) to take advantage of all the insights available through self-service analytics tools like Hadoop/Hive/Pig etc., then measuring how things are being used is crucial: measure how data is being used in your organization, and use those insights as part of a broader strategy towards improving everything else around it!

Self-service analytics and the democratization of data are two trends that are reshaping the way businesses operate. But what does it mean in practice? And how can you make sure your company is ready for this change?

But there are things you can do if your company doesn't have this kind of culture yet:

Instill an open culture where all employees feel empowered by using information from different sources (e.g., internal systems). This will help them better understand what good quality data looks like—and lead them into using analytics more effectively as well as gaining new insights into problems they've been trying unsuccessfully to solve before now. Provide training sessions so everyone understands what good practices look like when working with external sources (like APIs). When marketers are aware of the part that particular touchpoints played in a conversion, they are better able to allocate money in their future advertising strategies on touchpoints with a similar impact and to cut out efficient channels. This is according to the Marketing Evolution Report, but the problem is that data is often not used helpfully.

  • How can you make sure your business uses data effectively?

  • What does a data-driven culture look like?

  • What do you do if you don't have this kind of culture yet?

Empower everyone with the data they need to make decisions and create value for their customers, employees, and stakeholders. Be transparent about how your organization uses this information — what’s its purpose, who has access to it, and how often it's updated or updated with fresh insights from research or user feedback. Be open with your users about how you use their personal information (for example when asking them for consent). It will ensure they know exactly what information might be collected from them by third parties, which will help them feel comfortable sharing more freely with others within the company. 

But what does it mean in practice? What kind of organizational culture would see everyone using data effectively? And what can you do if your company doesn't have this kind of culture yet?

  • Be transparent about how you're using data.

  • Be transparent about why you're using data.

  • Be transparent about what data you're using and where it comes from so that everyone can understand how the business works and what their role is in contributing to this process.

  • Make sure that everyone understands how decisions are made based on analytics-based insights rather than gut instinct or hunches (which are often wrong).

How can you make sure your business uses data effectively?

There may not be an easy answer to this question – you can't simply change a decade's worth of business practices to adapt to new technology. Instead, you need to think about small changes you can make while you are working on long-term cultural shifts.

The first step is understanding your data: what it is and how it behaves. It will help inform your next steps in making sense of it or transforming it into something useful for decision-making purposes. For instance, if your company has many different departments with different reporting requirements then each department should have access to its analytics platform. The platform should allow all (and only them) access all relevant information from within one place rather than having every employee log onto separate systems every time they need some data refreshed or updated."

What does a data-driven culture look like? And, What do you do if you don't have this kind of culture yet?

These ones are easier to answer, at least with 5 tips to help your business start using data more effectively, including strategies that will also help you implement a data culture shift in the future.

  • Be transparent about how you’re using data – and why: Don’t just show off your shiny analytics dashboard. Make sure everyone has a basic understanding of data, so they can help make decisions with it. Explain how each metric helps your company run more efficiently, or what kind of insights you get from combining multiple pieces of information. Make sure people know where their data is coming from, and that it isn’t in use for anything else besides what it was intended for (e.g., using anonymized customer profiles for marketing purposes). Keep the most important metrics actionable by making them accessible through dashboards or visualizations; otherwise, users may not even know there are other ways to access this information!

  • Teach your employees how to use data

  • Encourage collaboration between different departments

Prioritize data cleansing and wrangling tasks over other projects (e.g., writing content). This will have less impact on performance but allow for insights into which areas need improvement most urgently or what actions can be taken promptly. If something goes wrong (e.g., finding out why someone who worked through their shift didn't finish so quickly). This is especially important for businesses that rely heavily on manual work processes rather than automation technology like CRMs or ERP systems. In these situations, some tasks still require human intervention even though technology could be helpful if desired; otherwise, these processes may become too complex for any person working within them without proper training since each person has different responsibilities depending on which role they hold in those departments.

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