How can you apply data analytics right now?

Imagine a SMART goal: enhancing customer satisfaction ratings by 20% within six months. It's Specific (boost customer satisfaction), Measurable (by 20% increase), Achievable (with targeted actions), Relevant (to our business), and Time-bound (within six months). This well-defined goal acts like a compass. It aligns our data collection and analysis efforts meticulously. Each data point gathered, every analysis conducted, ties to the larger mission. The goal also serves as a yardstick for measuring progress. Regular check-ins with your SMART goal let you fine-tune your approach, ensuring you're on track. Remember: a well-crafted goal isn't just a start; it's your guiding star. #DataDrivenGoals #DataInsights ????

Use tools to see sales, website visits, and how customers act. Improve work by checking how things are done. Make things customers like better. Plan for risks by looking at old data. Decide where to use money or people using data. Guess what might happen next and plan. Make ads better by looking at how they work. Help workers do better. Spend less money or use it better. Watch projects closely to make sure they're going well

In a data analytics program, half of the battle is won, if you’re able to - 1. Understand the as-is system 2. Identify the gap, stakeholders, and frame the problem statement 3. Understand how the data analytics solution will impact people and process In my experience, while talking to peer industry leaders, it’s quite evident that majority of data and analytics initiatives fail either due to the acute understanding of business or failure to align stakeholders on the solution.

One of the most common problem that data analytics teams in most organizations face are related to absence of SMART goals. Most the work done by Data Analysts doesn't result in any insight. Hence, when I saw a similar problem in my team, where at some point we were just making reports and delivering numbers to other teams, we started picking up weekly most cruicial problems that the organization was facing. The goal was to come up with some insight(s) by collecting and analyzing the data that could throw some light on the problem.

Goals need to be macro and micro level. Take improve your sales. A macro goal that's impacted by many smaller (micro) goals. 1) It can be impacted by the paid ads, your own assets like your email list and then organic media driven by things such as SEO and social. 2) Then there is how the assets you use to attract customers actually work. Are they user friendly? 3) Then there are the calls to action of your offers. Are they converting at a high or low level? Can you improve conversion by testing different approaches? 4) Finally there is retention of current customers and repeat purchases. This becomes your own media channel to reach customers. Measuring these micro goals helps identify ways to increase sales.

Collecting relevant data is an art. Data collection might be through different ways sometimes like web scrapping, sometimes it can be manual like surveys, interviews. And analysis will be correct only if data is correct

You're absolutely right. Data collection methods should align with your goal. Whether it's surveys, interviews, observations, experiments, web analytics, social media analytics, or existing databases, selecting the right sources and methods is crucial. Additionally, ensuring the quality, quantity, and variety of your data, while maintaining accuracy, completeness, and consistency, is vital to making informed decisions and drawing meaningful insights from your data analysis. It's the foundation upon which your data-driven efforts rely.

Data collection has an expense. Often leaders wave their hands and say: ??:Go do some analytics on that. ??: But that will cost X and take 3 months. ??: I don't want to spend that much or use that much time. ??: Then you don't want good data analytics.

Don't get disheartened by missing data. Sometimes, people expect comprehensive information to kickstart their analytics journey. It's better to begin with whatever initial data you have, even if it's limited but relevant. As you progress, you can decide to make efforts in getting more granular data. Occasionally, having high-level data can be beneficial before delving into granular details. Prior to decide to pay or to spend so much time to get extensive data granularity, ponder whether it would make the information more actionable compared to your existing data.

The data relevant to a business should be determined by its goals. As data volume grows, the complexity of managing it increases exponentially. It's essential to avoid the pitfall of collecting all available data just because it's possible. Instead, focus on a concise list of critical data, ensuring its accuracy, and having an infrastructure that supports scalability.

Another important part of your analysis process is to keep in mind that you may have to explain how you structured your workflow. Saying something like "Trust me, it works" will not be enough. From descriptive to prescriptive analytics, everything you do has to be explainable, at least at some level.

If you are new to analytics, you should definitely check out Power Bi. It is a powerful tool that lets you transform, analyze, and visualize your data with ease. You don’t need any technical skills to use Power Bi. You can learn how to create stunning reports and dashboards from the many free resources available online. Power Bi also makes it simple to share and publish your work with others. And don’t worry, Power Bi is not just for beginners. It is also used by experts who need advanced features and capabilities. Power Bi is the ultimate solution for your analytics needs.

Avoid the temptation to use data selectively to confirm your preliminary ideas. Especially when you have identified a clear goal and collected lots of data, it is easy to find evidence supporting your hypothesis by goal-oriented filtering and selection. Sometimes this happens unconsciously. You will need to look at the data from multiple viewpoints and through various filters, but try to do it in a robust and repeatable manner. Avoid the temptation to use data to tell a story that isn't there.

After collecting and cleaning your data, there are various tools to proceed with data analysis. If you prefer an easy-to-use solution, you can use Altair RapidMiner to visually design data analysis workflows without having to code. You don't have to be a machine learning expert an effective model, as RapidMiner provides AutoML capabilities that automate the model selection and hyperparameter tuning process, making it easier to make data-driven decisions.

Analysis is like statistics, everything is on a curve. 1. If it's money, look at where it's going or who has it. 2. If it's workplace time off or missing hours, look for prior issues, trends, or culture and current events. 3. If it's operational drag, look at who or what. Operations is like a flowing water in a pipe with different constraints. 4. Barriers to growth check your business EOSs. Point is, everything is related. E.g. Forecast says spend will be $1M over 4 quarters, spend is $900k after 2 quarters, what went wrong? Look at natural habits. Easy to drink water when the canteen is full. Whether it's a tool used or a process, be open to ask questions like what would I do here if I was given x and not questioned or expected to do x?

I've found it helpful when communicating findings to write the report as if you're a journalist. This is the part everyone should understand. Statistics aren't discussed except as reference numbers where you link to deeper parts of your document or tools (if they're possible to link - even better). So you tell the story why the sales happened not the dry numbers. For example; Our organic search team continue to outperform all other media (1) in terms of sales. Charlie Green The Head of Search puts it down to our recent changes to the website which makes it easier to read on mobile(2) and our continued improvements to the writing(3). 1, 2 & 3 here would all have numbers that back up this paragraph as appendices to the report.

The most important step in the analytics lifecycle. This is where all your analysis consolidates. If you cannot communicate the findings in the simplest manner then all your hardwork in coming up with the insights will go to waste. For example, you can say that if you do this, then you earn x amount in revenue or gain y% customers backed up by some charts and tables. But like any other skill, this can be improved too. The key is to keep in mind the problem statement or your end goal. Once you have that then you need to ask yourself whether you findings answer that question (positively or negatively). When you have perfected this, then you can convince your management and stakeholders with your insights and drive better decision making.

This may be one of the largest skill gaps in the marketplace. Most companies have built expansive analytics and reporting departments, with extremely talented statisticians and analysts. Unfortunately, in many cases these people do not understand the business side of things. This obviously limits their ability to analyze the data in a strategic and action-oriented manner. Moreover, in a lot of cases, these same people have never been trained to present data to a lay audience, which is a skill set in and of itself. Consequently, finding someone with an analytics background, that also has business acumen and presentation skills, is the best way to communicate your findings to a strategic audience who can ultimately implement them.

The key is, people only remember 2, maybe 3 takeaways max. Find what is most important to your audience, connect the data you found, and why they should care about it. Simple process, profound implications and impact. If it's a bad news report, eat the frog first, then spin it with 2 or 3 solutions on what should be done. If it's good news, say it's good news and provide a simple theme of victory or achievement connected to what was done right and who did them right. If it's neutral stick to the big talking point and KYA (know your audience). Each interaction is a bit like a stand-up comedy routine. A bit of simple truth and practicality goes a long way.

In my experience, one point to consider when talking about your findings is also to demonstrate your data in various ways, bearing in mind that your audience has different ways of learning. Some examples are: making it available through graphs, overviews of numbers only, text summaries or impact-oriented. Try to at least show your data and results in three different ways to make them available to each person reading and to ensure that everyone can process your data information.

Continuous improvement is the goal. You channel more resources to the top 25% of your activities by re-allocating from the bottom 25%. The important consideration is your goal. Branding for instance as a goal is not the same as sales so it's important not to mix the two activities. Otherwise you will cannibalise your own efforts and scupper both activities. Optimising the resources for the wrong goal can be expensive.

For example, think of a small "enterprise": an adorable lemonade stand that was set up by kids. On a scorching day, when business is booming, and they ran out of lemonade mix, their parents likely explained that relying on a single container of lemonade mix wouldn't be enough when their thirsty clientele needed more. The following day, they opted for two containers instead of one, allowing them to continue selling their sweet, sour syrup for an additional three hours. That moment exemplified data analytics at its finest. They took data, analyzed it, and then made a decision that ultimately boosted sales.

Absolutely agree! Feedback fuels progress in data analytics. It's not just about gathering insights but also about evolving strategies based on continuous evaluation and learning. A great reminder of the iterative nature of success in this field.

I'm going to take this one step further. Data should be part of a loop that defines our actions. Consider the financial planning process: Establish relationships, collect information, analyze information, develop a plan, implement the plan, Review and Monitor the plan. Then rinse and repeat. This is a wheel that's connected to goals and goals are connected to vision & mission.

Successful data analytics becomes embedded in the culture of the organisation and evolves into a key aspect of how the business operates. Depending on how it's applied, the mechanics of data analytics may not be seen, but the outputs should be monitored for the business to review, challenge, and evolve!

It’s not just about the tools you learn or use, it’s about the impact your work has on the business. Your analysis are there to drive business decisions and generate profit. Pretty dashboard do NOT equal impact!

Communication the insights is such a crucial part in proving value of analytics, but also in getting stakeholders on board with changes or improvements. It's important to be clear, but also keep things simple. Most end users will not be super technical or well versed in analytics and therefore won't need a really technical explanation.

Don't forget that there is often more than one data set involved in many analyses, and if so then it's important to create consistency between them wherever you can. For example, are your marketing systems and sales systems using the same definition for a 'current customer'? It's easy to make assumptions here but you'll save yourself so much time by checking these points at the beginning.

Learning data analytics will strengthen your information literacy. You will start to see reports that "x caused y" and know that this may be problematic to say (causation versus correlation). You will develop an appreciation for what you can confidently say with data, which will improve your insights as you make decisions in the workplace.