In my previous article, Introduction to Augmented Analytics, I discussed what it is and how it is going to disrupt the traditional data analytics. Now, let us take a look into the major ways in which Augmented analytics can improve your enterprise analytic capabilities
Top 5 Ways Enterprises Will Benefit With Augmented Analytics
Enabling Business Intelligence to be agile
Cloud-based deployments with regard to business intelligence platforms and advanced analytics. With the next-gen augmented analytics, data analytics will be carried out much more quickly and will also make optimum and minimum use of assets.
Biases, here, is used to refer to habits and routine. As human beings, we have a tendency to develop certain patterns to our processes. Thus, there can be a blindspot for data scientists due to their individual characteristics that causes them to not notice certain aspects. While this bias is not malicious, it can certainly lead them to overlook certain insights. When this entire process is automated, this kind of bias can be done with as machines do not have inherent biases.
Additionally, augmented analytics will make it possible to detect biases, trends and certain patterns before they pose any issue. Unlike manual exploration of data, it can recognize it irrelevant or false insights as well as avoid missing important data. With artificial intelligence aided analytics, even SME’s can gain from valuable insights as they are not limited by budget constraints that arises when they have to rely on data scientists.
Automating data cleaning and compilation for rapid analytics and reporting
The responsibility of analytics and reporting are usually assumed by the accounting department. They are in charge of compiling the data that the company receives on a monthly or quarterly basis and reporting it to the management. This process of reporting consumes a fair amount of time and as a consequence, the data is outdated by the time it reaches the management.
Technologies such as SQL databases, internet of things sensors, and centralized POS and CRM systems and companies made it easy for companies to access their key operating metrics in real-time (or very close to it). However, acquiring real-time data consumes a lot of time and money.This is because the process of Extract, Transform and Load(ELT) requires a large amount of computer engineering and data science works.
As you know, in most companies data is stored in at least three places – company owned platforms such as websites (collected via Google Analytics or Mixpanel or Hotjar), company’s point of sale systems (physical cashiers or e-commerce store system) or in the company’s advertising platforms such as Google or Facebook ads. The data that are collected from these platforms stored separately are in various forms, unconnected and are also incompatible with each other. Connecting all these kinds of data from various sources and integrating it into a mega data sheet requires a large amount of work from data scientists and developers. The merge data sheet, thus generated, could still only yield your company’s metrics such as ROI, Churn rate and the like
All these processes can be made simpler and faster with Augmented analytics which requires significantly lesser cost and technical competency from companies. Using an augmented analytic platform, data integration can be carried out seamlessly and the desired metrics can be obtained automatically.
A critical thing that you should avoid is comparing augmented analytics platform with a simple data acquisition service and thinking that they are similar. Some of the things that sets the former from the latter are:
- To meet the complex nature of deep integration, augmented analytics platform offers either a comprehensive interface to enable you to understand how much integration you want in your platforms or have a specialist who works with you to perform similar task
- Augmented analytics platforms performs an in-depth integration of all the data sources. This enables you to perform data analysis seamlessly across various platforms like an on-site experience analysis (Google analytics yielded data) of your users that comes from a particular FB ad(facebook yielded data).
- With augmented platforms, you will also have more metric options as all your platforms are completely integrated. An example is the metric ROI, which needs data from all your advertising platforms to get the cost, and your POS system to get the revenue. You can have the option of modifying the metrics formula for further customizing your metric sheet
Identifying changes in key trends
Data integration, which we have discussed above is only the first step in analytics. After you obtain the integrated data, there is still the challenge of deriving meaningful insights from it. The key metrics and dimensions in the mega data sheet for even small companies requires a lot of analysis and is time-consuming. A usual approach that most companies follow to avoid this analytics need is analytics heuristics, which basically means carrying out the analysis that the analysts thinks are important. However, in this process the company will inevitably lose certain valuable insights when they rely solely on their analyst’s intuition.
When you employ an augmented analytics platform, it takes only minutes for you to analyze all your data across all possible dimensional combinations and quickly understand the factors that are inhibiting or contributing to your enterprise growth. Moreover, an ideal augmented analytics platform can assist you to interactively go deeper into your data in case you get a cue in a more shallow analysis.
For instance, your augmented analytics platform may have found that there is a significant drop in your mobile traffic for your website. In order to understand the reason, your platform analyses all possible dimensions and may identify that the less traffic is because of the decrease in male mobile traffic from Seattle, who were using organic search to visit. With this information yielded by the platform, you will get an accurate idea of the root cause of the problem and can take appropriate actions.
Tracking the results of your actions and offer scientific conclusions on the efficacy of those actions
The valuable insights you have obtained about your company are futile if they are not used to formulate actions and those actions are tracked. A common complaint among managers and marketers is that it is impossible to systematically track and measure these actions as they are caught up with other business functions. Having said that, there are few digilent managers who look at their analytics daily and try to correlate the changes in their business to the changes in their data. However, they are often held up with changes in their data soon after taking an action. In such a scenario, not enough sample size is collected before they make the decision to stop or continue the action.
Using augmented analytics platforms, you can automate the tracking of actions that businesses take, which helps in making a scientifically based conclusion. These conclusions can be reached by any managers regardless of whether they have a background in statistics.
Gartner has forecasted that 40% of all data science activities will be automated by 2020 given the rapidly flourishing field of augmented intelligence. Though today augmented analytics is still in a nascent stage, industry experts believe that its growth will be quick and it will be the next stage of evolution for data analytics.