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Making Your Data Analytics Work for You

AdSpark Team - 7 years ago

One of the unique selling propositions of investing in digital and mobile marketing is its ability to collect large amounts of data. With these types of campaigns, not only will you see basic demographics about who have seen your ads, you will also be able to gauge their level of interest in your product or service. You can track how many users have clicked your ads, visited your website, and sent an inquiry because of your campaign.

Nowadays, there are so many different ways and sources from where you can collect data. It is everywhere and it is king. It dictates how much companies will spend in marketing, how many sales employees will they hire for the year, and why your customers should continue availing of your products or services. The challenge for most companies now is how they can extract and create information from said data and apply it to their businesses.

Thus, a need arose for data analysts and business intelligence experts – people who would be able to make sense of collected data and turn them into useful information the company can use. There are many stages and disciplines in creating data studies, such as creating purpose-driven data, and translating those data outputs into actions. Below are some data analytics tips and practices that you can apply to your business.

Purpose-Driven Data

Purpose-driven data is looking for information with an end goal in mind, usually tied with how you can improve business performance. This process starts with a little bit of introspection about the company.


Asking the right questions

Looking at your company now, ask yourself, “What is our priority?” Do you need to increase revenue? Do you need to reduce operational costs? Define your company’s top priority and use that as a reference to create questions that you want your data study to answer.

Without specific priorities or goals in mind, you could end up running around in circles. For example, a large financial company once decided to create a “data lake”, which was where they stored all of their collected data. The objective was to collect as much data as possible, store it, and see what patterns or insights can be pulled from the “data lake”. Although there were initial findings, most of them were insignificant and marginally relevant or interesting. Eventually, they decided to focus on a clear priority, which was how to reduce time spent in product development. This sharpened focus led the company to creating successful products which were sold to two new market segments.


Think really small… then big
One of the expectations, when a company forms a data analytics team and creates a data study, is that it can suddenly solve all of the company’s problems overnight. But the impact of analytics does not happen as quickly. Instead, it takes minute actions in the form of small steps for changes to be applied. Translate the insights and recommendations taken from your study into small and everyday actions that can easily be applied. These easy actions will eventually translate into small wins. The impact of this collection of small wins will lead to bigger accomplishments.


Embrace taboos
A common concept you will hear in data studies is “Garbage in, garbage out”. This is based on the idea that all data must be collected and stored in a uniform and organized matter. If some of the data on hand does not fit the others, this doesn’t necessarily mean that you should delete or disregard it. Data can come in different shapes and sizes. Don’t ignore them simply because you consider it poor, inconsistent, or dated. Every little bit of information helps as this could potentially help in forming insights and analyses for your study.


Connect the dots
Analytics isn’t just about collecting quantitative data and putting them in Excel. It’s also about having a multifaceted approach where you see the company as a whole, not just focusing on one or two aspects. The worst thing that you can do is to have “siloed data” where numbers relating to operations are not cross-referenced with numbers relating to sales, and so on. All actions in the company have a domino effect and are correlated, so pulling your data together across multiple departments allows you to have a clearer picture of the business as a whole.

From Outputs to Action

Now that you’ve pointed out what your company’s priority is and identified actionable items on how you can improve business performance, how do you apply them?


Run loops, not lines
The OODA loop was initially a military strategy coined by US Colonel John Boyd. It stands for the decision cycle of Observe, Orient, Decide, and Act. As Boyd often said, “Victory is the result of the way decisions are made”, and the side that reacts to situations quickly and processes new information more accurately will prevail. This decision process becomes a series of loops. For every new action or improvement in process, there must always be a loop of observing how people react to the change and seeing if there is a need to readjust.

This strategy has been adopted by world-class organizations to gain competitive advantage. For instance, Google consistently makes data-focused decisions, applies customer feedback into their solutions, and quickly launches products that people not only use but love.


Make your output usable and beautiful
No matter how much data you have and what insights you can extract out of them, ultimately this process leads to a point where one must propose their outputs to their team, colleagues, or bosses. Data visualization is also key in analytics. You must know how to properly present and articulate the insights pulled from the data to its audience, and make sure that it answers the initial question or purpose as to why there was a need for the data study in the first place.


Build a multi-skilled team
As with any team, it’s best to hire people who have various strengths. When you plan on forming your data analytics team, you must hire a combination of people with different strengths and skill sets to achieve maximum potential. Have a few employees who can collect and organize your data from different sources, then have some who are skilled in data visualization and are able to draw insights and recommendations from the data collected. Lastly, have someone senior who can spearhead and make strategic decisions for the team and ensuring that they stay on course.


Make adoption your deliverable
The purpose of applying data analytics to your company is so that you have information that can help improve business performance. But what if the rest of the company does not want to apply the insights you’ve garnered that could improve business processes or margins? This can happen due to many reasons. For example, it could be because people have been used to doing things a certain way and feel that if it’s not broken, don’t bother to fix it.


One of the ways you can mend this is to have representatives from each department be the stakeholders for your data study. For example, if the purpose of this study is to see how you can reduce man hours spent on launching digital marketing campaigns, then include some members of the campaign management and operations teams as your stakeholders. While they don’t necessarily need to have a large role in the study, it’s best to have regular meetings with your stakeholders and updating them on the progress of your project. In doing so, it becomes easier to apply other best practices in data analytics such as the OODA loop (getting stakeholders’ valuable inputs) and connecting the dots (having representatives from different departments aids in creating a better picture of the company).


Lastly, the most important aspect to data studies and analytics is actually getting the buy-in and support of upper management, as they have the power and ability to apply strategic activities and changes to the company. With their engagement, it becomes easier to make various teams adopt your insights and recommendations for improving business performance. It also helps if your company leaders understand the value in creating a culture that makes change and adoption possible. At the end of the day, this is the kind of culture that allows companies to have the flexibility and agility that keeps them ahead of their competitors.


Keers, Peter. 8 Steps to Make Data Analytics Work for You. 17 January 2017. <http://blog.onapproach.com/8-steps-to-make-data-analytics-work-for-you>.
Mayhew, Helen; Saleh, Tamim; Williams, Simon. Making data analytics work for you—instead of the other way around. October 2016. <http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/making-data-analytics-work-for-you-instead-of-the-other-way-around>.
AdSpark Team

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