Data is not just a tool, it is an asset to organisations. What makes data more powerful is the way a company makes use of the long stored inputs at its full potential. But many aren’t taking enough initiatives to reap the fruit out of it.
A report suggests that only 50% of an organisation’s structured data is used for making business decisions while less than 1% of the unstructured data is even utilized. It is now time to reorganise all the data issues and get more out of the valuable source. Strategically using data paves the way to improve the customer experience, become more agile at decision making, and predicts and acts upon future trends. Tech giants like Amazon, Facebook, Uber, etc are some of the strategic users of data profitably.
The solution for better data outcome can’t be similar to all because most companies are at different stages in their data journey. Big organisations are able to afford a data science team and connect all the networks through a sophisticated Internet of Things (IoT). But this is not the case with small companies that run on minimum revenue. Remarkable, data analytics doesn’t work luxuriously, it functions according to the smart work that people put in like asking valuable questions. Companies that use manual labour to do data inputs on an Excel spreadsheet could make more deliberate moves if they attain to use data properly.
Investing in data doesn’t need to be huge, but intellectual. Although organisations are investing millions on collecting and analyzing data with various data analytics tools, the real trouble is at using the data in an actionable and profitable way. A company can make use of the data by starting to interrogate the data they already have. However, it is up to the companies to frame the right questions to dip their toes into analytics.
A survey by Capgemini and EMC study has unravelled a set of facts about data and data decision making.
• The result suggests that 56% of the 1000 senior decision-makers in nine regions surveyed claims that their investment in big data will exceed the past investment in information management in the next three-years.
• Around 65% of them fear that their data investment will become irrelevant and uncompetitive if they do not leverage it.
• 45% of the people think that their current internal IT development cycles are not sufficient for new analytics and don’t fulfil their business requirements.
Better questioning of data brings better results
The technique of framing questions for data analytics can be equated to the questions asked in exams. The answer that students give in detail reciprocates the question that has proper specifications in it. The questions should focus on real business outcomes like reducing costs, driving new revenue streams or helping the business meet sustainability targets.
It is important to be particular when it comes to asking a question to data analytics. Don’t confine yourself to ask ‘How can our company become more data-driven?’ You are not the only person who is asking this. Almost everyone who jumps to the data world sticks to the question. Be more specific and detailed.
Gartner has predicted that 80% of the analytics insight won’t deliver business outcomes by 2022 due to ‘orphaned analytics.’ Orphaned analytics are analytics projects that aren’t strategically aligned to business objectives. So it is up to the person asking questions to keep everything in line.
Some tactical tips to look for while framing better questions
Use KPI to evaluate company status: It is reasonable to first evaluate the company’s business state and find its revenue position before doing data analytics. Use the Key Performance Indicator (KPI) to compare the company with it. Analyse the places that the data needs to be changed. KPI could be a smart investment to monitor the key performance indicators and provide a transparent overview of the company’s data.
Focus on framing detailed questions: Before framing a question, frame the goal and the decision making you expect it to facilitate. Blunder questions are often replied with blunder answers. So be specific in mentioning details to get a focus on key insights. For example, instead of making a blank question like ‘How can the company’s revenue be increased?’ ask, ‘What are the channels the company should focus more on in order to raise revenue while not increasing costs very much, leading to bigger profit margins?’ Or for an even better outcome, ask: ‘Which marketing campaign I did this quarter got the best ROI, and how can I replicate its success?’ These specific detailed questions get answers with reports on deep analytics.
Know to filter the data source: Data sources play an important role in predicting solutions. An anonymous source or a bad source of data can’t fulfil the need as expected. So learn to filter and clean the data by identifying its source and pick the field that the analytics needs to focus on. A company’s sales, finance and IT department are potential sources to show an insight of data. Therefore, stay open-minded and filter the data that is in need at a certain time.
Put similar effort on visualization: Visualizing data analytics in a catchy and easily understandable way is also important. People get attracted to stuff that is uncomplicated and impressive. So put a similar effort in visualization as well. Even when the data analytics efforts are huge, people tend to ignore the outcome if it is poorly presented. Make sure that the audience gets reassured that the data analytics you provide is correct, important and urgent to act upon.
Bridge the gap between data scientists and marketing experts: Data scientists are not necessarily experts in marketing, manufacturing or yard management. Therefore, collaboration between a data scientist and business expert to establish shared goals and drive business outcomes will have more effect. Make sure to fill the gap that these two may leverage before concluding.
Making companies data-driven involves setting more strategic objectives before diving into a data analytics project to acquire accurate predictions for the future. The data involvement in a company’s statistics provides a measurable impact on performance, profit and productivity. Data analytics provide a lot of positive outcome for a company. But the people managing it should make sure that it is utilized properly to the fullest.
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