Big Data has played a role in helping a number of industries immeasurably, as its role in the business world is becoming more important with each passing day. However, even though the utility of Big Data in a professional setting is immense, there are very few organizations capable to utilizing the technology at an optimal level to boost their operations.
A large number of companies fear that they will make mistakes with the technology, which stops them from moving forward with Big Data analytics and maximizing its value. This is because, when used poorly, Big Data analytics can make false predictions for the future. However, when implemented correctly, Big Data offers a lot of upside to an organization. Combine its capabilities with a focused vision and a competent team, and there is a good chance the technology will bolster your company’s operations and profitability.
Keep reading as we will help you develop such vision with our insights on how you can be successful with Big Data.
1. Skills matter more than technology
It’s no secret that without the right technological tools, it is nearly impossible to succeed in a growingly competitive and sophisticated business world. Nevertheless, technology alone is not enough to help you attain this success—having the skills to operate the technology properly is also needed. While talking of Big Data, your team’s skills are far more important than the technology itself since technical ability has a very small role to play in Big Data analytics. The Big Data analyst must have know how to come up with right business questions, developing a clear forward path to make the best of the technology. The analyst must also be competent enough to parse and analyze the unstructured data through pattern recognition and hypothesis formation. Eventually, the analyst should know how to use the appropriate statistical tools to generate a predictive analysis. It is not necessary for the analyst to have all these qualities before joining the organization. Instead, the organization must conduct workshops every now and then to update analysts on the latest uses of Big Data to add value to your business.
2. Run necessary pilots
Big Data Is generally adopted by firms that want a predictive analysis of market trends that they can use to to plan for their future. Such predictions are not always unearthed in a manner that ends up being useful to your organization. If the predictive data cannot be applied to your business, Big Data will not yield the fruits of success that you seek. Therefore, it is highly advisable that when looking for data-based predictions, you should run a pilot to determine whether your predictions can be applied to improve your systems or not. Doing so will not only help you rectify your errors, but will also help you redefine your prediction in a manner that better suits your market needs. Furthermore, running a pilot will also reveal any weak points on your plans from their inception through the execution of them. Thus, one pilot will strengthen the quality of your operations, as well as the overall strategies of your business.
3. Formulate targeted analysis
It is imperative that the data you compile from the market is raw and unstructured. The amount of data available is expected to grow eightfold over the next five years, according to Gartner, most of which will be unstructured. Keeping this in mind, organizations must ensure they are ready to parse and analyze the data in a manner that will be beneficial to your business. Targeted analysis is key as one dataset may be used to unearth insights about multiple topics, while other pieces of information may not need to be extracted as they may not be relevant to your goals. Know what you’re hoping to achieve before extracting insights from your datasets, and then proceed to analyze the data. Having the right technological tools beforehand that you can use to store and analyze data is key. Always keep a backlog with indices for relevant interpretations of the data, so that when you need to extract information from the same dataset in future, it will be readily available for any future analysis.
4. Extract the best data possible
Even a small dataset can sometimes prove to be effective in developing predictions, while it is also equally possible for big sets of unstructured data to lead you nowhere. Aim to always narrow the focus of the data you compile for analytical purpose without compromising the robustness of the predictions. Going this route will save you plenty of time, while also helping you attain an accurate and actionable prediction. Don’t continue running massive sets of unstructured data in the hope that it will definitely lead you to a robust prediction as this is a waste of your time.
5. Keep predictions within your organization’s operational ability
Do not aim for predictions that lie outside the ability of your firm. Not all organizations are equipped with the skills and technological prowess to make the most of your predictions, so make sure your predictions are targeted within your means. Most organizations have a limited amount of wiggle room and the challenge is to come up with predictions that your organization is comfortable with. Do not exhort unnecessary operational pressure on your organization because it will only hamper the pace and confidence of your workers.
6. Be adaptive
The best results in Big Data analytics are achieved when the most actionable predictions happen to be affordable for your firm. As discussed earlier, don’t place an unnecessary burden on your firm in the hopes of achieving the best prediction possible. Instead, bring adaptive changes to your firm slowly in a way that will help it accommodate the best of ideas. When these ideas match the capabilities of your firm, great results will be only an arm’s reach away.