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You've probably heard of big data, AI, machine learning and all the other 4th Industrial Revolution terms which get bounced around nowadays. Although they all work together to create improved customer experiences and help companies make good on their deliverables. Data analysis, which is the analysis of big data, into intelligible, insightful segments is one of the more useful and immediate benefits of having today's computing power and vast datasets.
Data analysis contains a significant human aspect to it because people must be trained on how to interpret the data in useful ways, based on the context in which they find themselves. For instance, in the case of an anomaly such as an international pandemic, datasets will change drastically, for a medium- to long-term period. Although AI and machine learning are great, these types of computations learn from simulated data, which is usually extrapolated from data which existed previously. And since anomalies are just that − anomalies − it stands to reason that a machine will not have the capacity of a human being to analyse the data contextually and make suggestions and recommendations accordingly.
Data analysis will continue to grow as a desirable skillset for any employee whether they need to learn to analyse, visualise or interpret data. There are some immediate benefits to having a data analyst on your payroll:
So many actions require you to have at least a partial dataset, if having a complete one is out of the question. How did your last marketing strategy perform? The SEO strategy of your website? What about your share projections for the next three years if you're a listed company? All of these variables require tangible data to be available so that informed decisions can be made. Imagine the strain on your company's time-based resources, primarily labour, if you cannot account for performance and meeting targets using actual data. All you are left with is estimations, which take a long time to create and even longer to extrapolate correctly, since by their very nature, these will lead to glaring inaccuracies.
Say we use the stock price example: your financial analysts are working in a non-data environment. No data exists for previous stock behaviour, even to the last week. They use the stock price from today to try and guess what it will be in the next three years. This makes no logical sense and it is a waste of time. It has less of a chance of being accurate than someone guessing the change of seasons simply from being alive. In the case of judging the seasons, the dataset is years of living, as informal as that data collation may be. But in this thought experiment, your financial analysts have absolutely nothing to work off and they are making one of the major decisions that will affect your company in the long term. Their time could be spent better with data on hand.
Have you ever tried to buy a shoe without knowing anything about it? Well, imagine if you had to do that completely in the dark and without being able to touch the shoe before purchase. This seems like a ludicrous way to make a purchase, and this is exactly how operating without data analysis within your business is like. You need to try on the shoe. This is tactile data. You need to know if it is a running shoe, business-formal shoe or leisure sneaker. This is contextual or use-case data. This is just an example of how even the most minute actions in life are, in fact, data centric.
So, if buying a shoe can be that complicated in terms of data collation, what about operating a business? Whether you create a product or offer a service, managing your data could mean the difference between making a profit or making a loss. You can use data to benchmark performance and find out key areas where you can increase efficiency and enact cost-saving measures. Let's say you are the manufacturer of the running shoe. It's not a "If the shoe fits, they'll wear it" situation. You need to do market analysis in order to know where your product stands. What are your competitors' USPs? Are there enough runners in the region you're targeting to make the venture profitable? There is no use going through the entire logistics chain, to deliver products which will not move off the shelves, when they could be moving much faster in a different region. Only data can tell you that.
People skilled in data analysis can save you incredible amounts of money just by collating patterns and trends and then breaking that down into functional recommendations. The onus is on you as a business owner to upskill as many people in each department to be able to analyse the data relevant to them. It is of no use for the engineer who creates the running shoe's revolutionary breathable material to see the data related to its sales in Honduras. He needs to be able to analyse and interpret data which is key to his performance. The fastest way to get people skilled at making these sorts of analyses is to facilitate their learning through online courses in data analysis. Not only will they come out the other side better equipped to make the decisions which will move your business forward, but they will do so quickly and frugally.
Often, the data produced through whatever form of collation you undertake will introduce new insights. You may find that a product which you had been ignoring in terms of marketing budget is more popular in a specific region than another. Or, maybe a service you offer is gaining more traction as a result of a change of season, a new building having been completed close to your business, or any other variable. These are not insights which you can see 'off the cuff' and require hard data to prove, particularly to stakeholders. If you want to scale your business and need investor buy-in, a data-driven approach to your operations will always stand you in good stead.
New insights are always an opportunity: whether in bettering your operations through efficiency, cost-cutting or better targeting; leads on new products you can develop; new customer relations opportunities; or hiring opportunities − the list is endless.
According to Malcolm Gladwell in his book Outliers, it takes 10 000 hours for someone to master a specific action, whether that be scuba diving, guitar playing or playing soccer. Now, consider this: do you think all your employees, suppliers and various stakeholders have completed the requisite 10 000 hours in their given roles? And if they haven't done so, do you have the time to wait for them to be masters at whatever they are doing? Of course not. In this 'time is money' age, any time spent not being productive at the highest level is wasted time. This means for all those employees and other stakeholders who are not yet masters of their craft, and mostly never will be, errors will be made. And these errors won't creep in slowly. On a daily level, you may face up to 12 erroneous actions made by all those involved in your business. Some you may allow: the security guard who forgot to lock the padlock on the warehouse, but your integrated security measures made sure that your assets were protected. It's one night. One silly mistake. However, what about an accounting error? You can't afford to have money unaccounted for when you need to have your books audited. Data analysis bridges the gap between human error and measures to correct those errors.
They say, 'data doesn't lie' and it's this very facet of the data analysis function which you should consistently leverage because it's true. Data might be skewed based on the size of the dataset and that may affect its accuracy at the time of extrapolation, but the figures themselves are factual. If the figures say that sales have risen 13% in the past 3 weeks, then they actually have. It's up to the data analyst to interpret the results to show the daily pattern, perhaps what time those sales were made and where. But this can only help to further strengthen your operations. Where errors are involved, good, thorough analysis can help you to either mitigate any such errors ever happening again, or immediately correct the errors if they still exist.
With computing power where it currently stands, it's possible to have live datasets that are ever-changing and allow for immediate interpretation, and interactions. This is particularly important in industries where live data is a must such as the stock market. Traders do not spend money on a stock's position from two hours ago. They need a minute-by-minute update. This can also help you if you are involved in other businesses such as shipping or other logistics. You can trace drivers in real-time and adjust their routes if there are any accidents on their way. Each driver's route data can then be collated and analysed to find the best compromise between mileage and efficiency. Maybe the new route had a lot of inclines and the driver had to accelerate more, burning more fuel. Is there a different route he can take next time something similar happens? This is only possible because your operations are both static and dynamic: static, because the start and end destinations remain the same over a long period − you supply to a specific company, based in a specific place; and dynamic, because now you can use onsite, real-time data analysis to improve performance. It's difficult not to see the benefits of such a data-driven approach.
Nowadays, there are many specialised companies with high budgets that you have to contend with. These titans of industry often use data to its full advantage, possessing teams of dedicated data analysts to analyse their finances, customer relations, sales and marketing. They have created the template for smaller companies to follow because they have proved that using data in sales and marketing campaigns works.
For example, Apple's marketing of the original iPod. They collated data which showed them that they should target tech-savvy millennials who would be drawn by two very specific value propositions:
Here they not only challenged the status quo of mp3 players at the time (albeit the iPod's actual format was AAC) by offering a considerable amount of more space for songs; but they also figured that since most of the world's population use a PC over a Mac, they should open up their product to that market as well. Add to that the now-iconic silhouettes of various cool people jamming, the company hit the jackpot. Rest assured; this was not an expected win. Without thorough research done beforehand and collation of that data into useful actionable metrics, the entire rollout could have gone the way of previous tech ventures which failed.
This final benefit of data analysis is more important now more than ever. In this world where everybody has a portable device capable of connecting to the internet in 3 seconds or less, it is important that you understand people's individuality. Google offers people a search engine to find whatever they need across the indexable internet. YouTube allows them to search videos in much the same way. And Spotify allows people to search genres, artists and even moods of music. People already have all of these ready-made, individualised experiences at their fingertips. Why would they support a product range or service which does not meet criteria which can so easily be met by a smartphone?
This is where data analytics plays a role. It's not to say that you should change your customer relations strategy on the fly, but you should remain adaptable. This means being ready to allow customers to feel more connected to your products in a more individual manner. Maybe you are creating a product which will be used on an ongoing basis, such as a forklift or earthmover. Why not try to add value to your B2B customer by emblazoning their logo on the side of the vehicle? You know you created the vehicle as the manufacturer, as do they. But this creates a sense of inclusivity and personalisation that is difficult to replicate. It makes the customer feel valued.
There are plenty of other ways in which the data you then get from the user experience can feed into your future customer relations strategy. If the above logo print was well-received by more than one client, you now have an active dataset to work with and you can look at other products which you manufacture for their suitability. As such, you are adapting.
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