We live in an age dominated by the production, harvesting and analysis of data. Data can take many shifting forms, but one thing is certain: humanity is producing more of it than ever before. The world of business has been drastically changed by the so-called data deluge. Making sense of the huge quantities of usable information produced on the internet is especially beneficial for people looking to foster business growth.
Enter the data analyst. Data analysts are the financial oracles of the modern world. They can’t quite give out glimpses into the future like the mythical oracles of the classical world, but they can do the next best thing: make sense of what the future might bring.
Data science – which includes data analysis – was born when the ancient field of statistics was combined with the modern field of computer science around the middle of the 20th Century. In his 1962 seminal work The Future Of Data Analysis John W Tukey wrote of the need to create a distinct science out of the interpretation of data, and therefore set wheels in motion that would see data analysis rise to prominence in a world where data is all around us.
Data analysis has myriad uses in the world of business. This article explores some of the most important of these uses.
Making Sense of the Market
Data analysis is the key to really understanding a market. Market research is absolutely essential in any field of business. An understanding of audience habits and cultural touchstones, satisfaction with rival products and specific areas of desire can be achieved through market research.
The types of quantitative data that should be analyzed during market research are:
The Size of a Market
Just how many people can you ouch with a product or service?
Your Potential Share of the Market
How many rivals control a percentage of your target market and how much can you expect to disrupt?
The Demographic Makeup of a Market
What age, gender race and social status do your potential customers, clients or collaborators belong to?
The Source of Data
Where is this data coming from? What are the biases in your market research?
The types of qualitative data that should be analyzed during market research are:
Values and Beliefs
What are the core values and beliefs that drive customer or collaborator engagement?
What do market trends indicate about the potential of a business to grow?
What kinds of things have been said about previous releases, campaigns and collaborations? This can include the coding of review data, feedback data, complaints data and any other kind of interaction with a consumer base.
Looking Back at Performance
All businesses need to have a way of looking back at their performance during a release or campaign in order to learn from successes and failures. Data holds the key to effective performance review. Both qualitative and quantitative data can be used to figure out just how successful a project has been. Good data analysts will also be able to figure out what areas specifically need to be restructured in order to produce better results.
All senior staff should have a base knowledge of data analysis so they can look back on performance with a scientific eye. Managers that are not working exclusively with data can still take a data analytics course online in order to boost their knowledge and give them the ability to understand trends in performance data.
Big data analysis is often used to create trend projections. Using huge quantities of data, analysts can work out the likely fluctuations in a market or system. This enables a company to strategize more effectively. The best trend projections use data that has been collected – or logged – over a long period of time. This allows for as many variables as possible to play out.
Of course, this is not a foolproof process. All sorts of deus ex machina can change the fortunes of an industry in ways that simply cannot be predicted. The larger the dataset that is analyzed, the less likely a surprising factor will come into play. Data analysts now enlist the help of Artificial Intelligence in order to swiftly and accurately make sense of datasets that would have been too large to practically interpret in the past.
Business strategy is becoming ever more reliant on big data analysis. Strategic thinking is far more safe when it is backed up by statistical surety.
Data analysis has a part to play in risk mitigation strategies. Identifying points of potential conflict and failure is one of the key roles of a senior analyst. Companies rightly shy away from areas where they could be entangled in market failure or controversy. By analyzing the qualitative data surrounding failure and success rates and composing a risk mitigation plan, a company can make good use of the huge quantities of data at its disposal in order to protect itself from disaster.
Of course, there is far too much data available to take it all into account. A data analyst must choose the right data, the right way of analyzing it, the right person to present it to and the right timetable for interpretation. The abundance of data can be a hinderance from a risk management perspective. Risk managers need to be presented with concise interpretations of data that allow them to make strategic decisions without getting lost in jargon or conjecture.
Data breaches can be incredibly costly for businesses. They can erode or completely destroy customer confidence in a company. Data is useful, but it is also extremely sensitive. Careful data analysis can help a business identify where possible security leaks may occur. This kind of preemptive planning is absolutely essential if data breaches are to be prevented. Data analysts can study the wider trends in hacking and data theft in order to create statistical models. These statistical models can then be used to identify future vulnerabilities in an organization’s security apparatus.