How to Convert your Company’s Big Data into Actionable Data
Updated: Feb 6
The amount of data that is generated today in an hour is astounding. It can be a valuable asset for those who know how to leverage this data; for others, not so much.
But the real question is how do you use big data to achieve the results you have been hoping for?
A lot of companies have now started investing in big data technologies due to the competitive edge that it provides. But simply investing money in platforms and tools won’t solve the problem. And only those organizations which know exactly which piece of information will get them what will be able to reap the real benefits of the data.
Why do companies need Actionable Data?
The data collected on the day to day basis assimilates together and amounts to be such a humongous quantity that significant insights are often overlooked. As companies compete for the first spot in their respective industries, they often don’t give much thought to the tools they are replying and thus end up losing on the competitive front.
For companies which are looking to adapt to data-driven mindset, it is very important for them to set clear corporate goals about what do they want out of data and which kind of data are they going to focus on mining.
The kind of data, that drives corporate decision making and helps generate insights that can help a company move up the ladder in the market, is called actionable data.
This kind of data has implications on all fronts, regardless the industry you are in. It can help doctors provide a life-saving diagnosis. Retail can use this to increase their performance and efficiency on various fronts by targeting their customer’s demographics and finding ways to providing customer service in store. Machine learning and AI tools can rapidly capture, filter and analyze data to expedite response time and determine what kind of info is most relevant when.
7 ways to Convert your Company’s Big Data into Actionable Data
One of the biggest mistakes that is made while collecting data is the lack of a solid object behind it.
What kind of data needs to be collected? Why is that needed? How much of that data is sufficient? All these questions need to be answered before one can set the data collection tools to work and keep the scope of data usage in mind.
Here are a few key strategies, suggested by data scientist Dr. Michael Wu. which can help turn raw data into actionable data:
Determine the business problem you are trying to solve
Start with a business problem. Collecting all that data to play safe is smart because you don’t know what kind of problem you might encounter in the future, but it is very important you start collecting the data with a problem to solve, to have a clear direction and scope. In doing so, you increase the shelf life of your data. And if the data you collected is able to solve a problem then you know what kind of attributes or insights are useful.
Start with a descriptive analysis
There are 3 classes of data that can be used to move raw data into actionable insights.
Descriptive analysis– Computer descriptive statistics. All the data collected from social media falls into this category.
Predictive analysis– A model that uses existing data to predict data that we don’t have yet. All trends lines, influence scoring etc. fall under this category.
Prescriptive data– A prescriptive model that uses the existing data, as well as action and feedback data to guide the decision maker to the desired outcome. Since prescriptive models must be actionable and have a feedback data stream, social analytics are rarely prescriptive.
Computer sentiment with prescriptive analysis
The simplest type of predictive analysis that everyone knows about is trend line. You can look at the data and then see if you will be able to follow that particular trend in the future as well.
In social media, there is some kind of predictive analysis that is known to people, like for example, sentiment analysis. Here we don’t ask people what they prefer. We just pick up cues from social media like, “I love Apple or I love my new Android” and use that to build a model. It incurs that when people next time use this kind of phrases, it typically means that they have a positive inclination towards that brand or product.
Meet KPI with prescriptive analysis
Google map is the simplest type of prescriptive analysis. You want to go somewhere and it prescribes you with a route. With prescriptive analysis, you can prescribe what you need to do and what you need to focus on to get to a certain level of business performance, such as achieving the highest customer satisfaction etc.
Accelerate decision making with clean data
Data collection is a messy thing. Information is compiled from different sources and not all data that is collected is normalized before analysis. Cleaning or correcting any dirty data helps the analyst remove any info that could lead to any miscommunication or misdirection in decision making. Normalisations allow us to see exactly what we are looking for and reduces time and effort in reaching the actual result.
Don’t underestimate the scope of work
Big data has so many enticing benefits that many companies just jump in without taking in the full scope of work.
For example, not having the appropriate tools can hinder the progress if they aren’t ready to handle that mass amount of data. There are other concerns also, like not asking the right questions of failing to analyze the right type of data.
Employ automation to simplify data analysis
Once the data is clear and the infrastructure is ready to handle high-performance data analytics, companies can turn to automation. Automation aids businesses during the data collection period to leave more time for analysis that manual work.
Looking for assistance to transform your company’s data into actionable data? Contact us at Datahut, your big data experts.