How Data Analytics Has Changed the Tech Industry

Our entire society and history can be tallied up into numbers and lists. To the naked eye, these lists are entirely incomprehensible but give that data to a master in analytics, and truly insightful deductions can be extracted. This applies to every industry on earth, but hands down the industry that has seen the most significant change and benefit from data analysis is the tech industry.

Data analytics applies both on small scales and on large scales. On small scales, it’s analyzing the code and user analytics to ensure that customers or your employees can continue to use your systems effectively. On a grander scale, however, you can deduce societal behaviors that were, until now, entirely unknown.

Data analytics and Big Data have completely changed the world, and they have been instrumental in the tech industry for these key reasons.

1. Predictive Data Analysis

Data can be used to inform current business practices, but that is just scratching the surface. Using predictive models, data can also be used to track trends and understand the future direction your business needs to go.

Data can help you recognize trends. It can help you understand what business points are failing and which ones are succeeding. Combining data analysis with smart business management will future-proof your business.

The internet has sped a lot of processes up. Whereas in the 1920s, the average lifespan of a business was 67 years, today it is just 15. Companies rise and fall faster than ever seen before in history because newcomers and disruptive business models can arise at any time.

Global audiences also play a more significant role. Whereas before businesses only needed to work to attract and keep customers in their local communities, today, the internet has increased competition. Building loyalty is harder than ever when other companies can come in and offer something better at any moment.

Your only hope is to use predictive data analysis to ride the trends and lead your industry again, and again.

2. Real-Time Data Analysis

Machine learning and smarter AI systems are at the heart of real-time data analysis. Instead of only relying on data analysts, now computers can take, organize, and take action based on the data in a split second.

This does not negate the benefit of data analysts. Someone with a Master of Analytics from UNSW online is still essential to provide contextual support. AI data analysis can help offer personalization and improved user experience, but when it comes to making creative decisions, AI cannot compare.

After all, future-proofing your business using data often requires a creative and out-of-the-box strategy. Save small-time processes to the AI, for example, providing users with more custom content to their feeds. Leave life-changing business decisions to the analysts.

3. Streamlined Business Management

Data will show where your business is weak. It will show where problems are and even give suggestions on how to fix them. For example, if you have a huge cost of waste, then the best solution is to use your data so that you can streamline production.

Previous production and sales trends can help you produce the exact right number of products depending on the season. This way, you reduce the cost of waste and improve your overall revenue.

4. User and Customer Data Analysis

Machine learning and AI can do wonders to help improve the personalization process in real-time, but that doesn’t mean you can forget about your customers. You must analyze overall customer behavior to determine ongoing and prevailing trends both in your own business and in your industry.

Be the first to capitalize on a new trend, and you will see huge returns.

The same concepts apply to user satisfaction. Analyzing how they interact with your site will give you great ideas on how to improve their experience and your bottom line.

5. Effective Cost Analysis

Data analysis doesn’t just apply to your own data. You can use data analysis to predict changes to your business as well. In the tech industry, this can be done by running predictive models to understand the impact of new lines of code or how new technologies will impact your business or customers.  

Data analytics has changed everything. They say you need to know the past to prevent future mistakes and at its core, which is the goal of data analysis. The only difference is the additional steps that must be taken to first convert raw data into understandable and actionable advice. As a data analyst, you will be able to do this with a variety of goals in mind. Streamline your business and work to improve it for the future just by using the data you have available to you.