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Artificial Intelligence is increasingly becoming mainstream and big data is being applied to every aspect of the business enterprise. As organisations rush to extract value from Big Data they increasingly struggle to use it for key executive management decisions in the real world. The main question still remains what Do's and Don'ts must be checked while handling the enormous Big Data, here is a brief snapshot:
The days when technology was restricted to the chief information officer are long gone. Today, an organisation cannot simply give all the information to the analytics team and ask them to convey the future course of business actions. The top management has to become data owners and strive to become curious about data in addition to leading the analytics team to address real business concerns and work in tandem to make sure all the data is targeted as an intelligent investment.
Today's data is available from multiple sources which include social media, cameras, smartphones, sensors, payment systems and so on. The next question is what to do with this data. Business analysts, who can read the information to foresee data patterns with an aim to spot market opportunities, identify problems, come up with solutions and become change leaders. Organisations need these business 'translators' who may not be fully-fledged data scientists, but are proficient in analytics and know how to apply numbers and data for the benefit of the business. Companies need to avoid getting lost in translation and invest in training these business translators in terms of leadership skills, to bring a championing change throughout the organisation.
As business organisations become more complex, data legacy issues are cropping up. There is a natural urge to capture the business's legacy data subsequently to get lost finding its intelligent application. Organisations must resist the urge and identify where the data is coming from, is it from sales, social media, 'open' sources operations, or elsewhere. There is an increasing need to have specific business applications in mind, making sure an organisation's data strategy connects directly to the analytics. Business enterprises must avoid the temptation to build complex models and decide their business priorities before acquiring any data. This will enable them to develop a sound process and practice, good data governance which can be augmented and refined by linking new and different datasets to generate intelligent business insights.
Organisations must set their priorities and identify the most promising sources of value to the business. They must analyse which processes are important and identify as many use cases as possible. The next step is to investigate on new data and techniques to generate new insights. Setting priorities are based on the potential financial impact, likely speed of implementation and the business suitability.
One of the most common reasons that data analytics still remains a novice trend is that the people who can put it to its best use lack the meaningful access to it. Avoiding this pitfall requires a three-step strategy which starts from making sure that the data is accessible to as many people as possible, getting away with organisational hierarchies that may impede access. There is a need to drive consensus on the validity of the data, so there is agreement on a single source of truth for the business. Lastly, to build an equal access, there is a need to develop an egalitarian culture whereby everyone is allowed to play with the data without fear or favour and try to generate new ideas.
Earlier, companies of all shapes and sizes guarded all market and business insights closely to retain the know-how and competitive edge. This is no longer necessarily the case, there are plenty of industries where data sharing, increases the comprehensiveness and enables individual businesses, which may be operating in different segments of the market, enhance their offerings and create a greater business value.
To get the most insights from the data, an organisation needs to foster a test-and-learn culture where senior management sets out the vision, and employees are encouraged to identify the opportunity areas and develop proofs of concept and then deploy the data to analyse results. This is a learning process, which means a no-blame culture; where organisations are actively looking for new and possibly highly counterintuitive insights and testing them. If this hypothesis stands up, enterprises may move on to look at implementation and, if it doesn't work, this is treated as a valuable lesson for the next iteration.
Many organisations try to make the data fit over a particular business agenda or pre-existing idea. This is a natural tendency as the human mind takes a long time to learn what works and what does not and thus tries on different combinations and finds it hard to let go of a winning formula. This is where the machines come in. Data analytics comes up with amazing speed when something is not working, whether it is an actual business situation or a test-case scenario. So, no matter how an organisation is attached to its idea of what the next big product launch should look like, it must be prepared to walk away as soon as the data tells a different story.
As a data analyst, one lives in a world where they are constantly and quickly absorbing new information. Make assumptions and doubt the occurrences. Thus, the data analyst's mindset is relentlessly self-critical and always questioning. One needs to exude confidence and authority to fight doubts and march ahead. The trick is to be able to balance the two; to recognise that nothing is guaranteed when future events are concerned. For a data analyst, it is an intrinsically complex communication challenge as they need to be able to see the world bottom-up and communicate top-down because they are driving the process of change. And that is something the machines will never be able to do.
