Ask any business about their data analytics strategy, and you may hear, ‘we are already doing it.’ They might mention that they aim to address market trends. Or maybe it’s consumer expectations.

But how exactly is this done?

Data analytics is the broad field for collecting and assessing information.

In the corporate world, data analytics can optimize business processes and provide meaningful insights into the wants and needs of consumers. 

In this post, we will not only discuss ten things you probably did not know about data and analytics but also show how you can use them to develop a killer business analytics strategy.

1. 1. Data analytics is global
   2. Data is never clean
   3. Big data is just a tool
   4. Data analytics can and can’t be automated
   5. You can sell data
   6. Cloud is essential for data and analytics
   7. X analytics makes data processing easier
   8. Data is subject to human error
   9. Cleaning and preparing data is inevitable
   10. Presentation is key

Data Analytics Is Global

If you have not already considered integrating data and analytics into your business, maybe it is time to hit pause on your business analytics strategy.

With more than 60% of people using data and analytics globally to improve processes and cost efficiency, the field of data analytics is becoming something many businesses, companies, and organizations are exploring.

For example, big data is one of the most scalable data analysis tools, and it is evolving the ways businesses are living up to their customer demands.

But data analytics goes beyond the retail industry. From healthcare to art and everything in between, it is becoming the pinnacle of the 21st century. 

Fields like trade, agriculture, communications, and banking use data and analytics to make conclusions about improving the quality of services offered, software and ICT systems used, and processes employed in practice. 

Data analytics has become so global that we interact and indirectly use it in our everyday lives. In fact, did you know that the current social media trends that we may participate in are a form of data analytics?

Now, that’s a transformation! 

Data Is Never Clean

Hate to burst it to you, but chucking information into a system and receiving it untouched is simply a fairy tale. 

In reality, data is never completely clean. Data in its raw form (unprocessed) is affected by various factors such as bias, validity, reliability, etc. Due to this, raw data is not helpful to anyone and has to undergo a process where it gets translated into useable information. It gets collected, filtered, sorted, analysed, stored, and presented in an easy-to-follow format.

By processing data, you effectively remove any impurities like bias allowing data to be accurate and reliable.

Data processing is crucial to businesses as it creates better business strategies and helps them stand out from their competitors. By converting data into presentable formats like documents, graphs, and charts, stakeholders and employees within a business can comprehend and analyse findings. The result leaves greater interest in an organization and builds more transparent decision-making. 

While processed data is more refined, it is not completely clean. By processing data manually, you are effectively chiming in your opinion. And, AI-based data processing algorithms can only be so perfect. In either case, various factors still impact data. 

But there is no need to worry! As long as you process data to an extent, it should be safe to use. 

Big Data Is Just A Tool

The big boy. The final boss. 

Whatever you want to call it – big data has become incredibly capitalized. And for a good reason. 

Big data refers to large, diverse sets of information that grow exponentially with time and are hard to process and manage through traditional data-processing software.

It is stored in large databases and offers businesses insights into bulk data like demographic, consumer habits, and product discovery. In fact, by the end of 2018, more than 90% of businesses planned to use big data.

While big data has been around for a while, its popularity only rose in 2005. As people realized how much data giant tech companies like Facebook and YouTube were generating from their users. Ever since, big data has become more widespread, offering long-term scalable information for businesses, companies, and anyone to consider.

However, the hype around big data is overrated. After all, it is only a collection of data analysis tools like many other analytical tools out there.

And as analytical technology continues to grow, there will be greater possibilities for visualizing big data sets through consumable and easy-to-follow tools, like graph databases and google charts.

So, it is not surprising that big data is an essential element of data analytics and will continue to be for years to come. 

Data Analytics Can and Can’t Be Automated

Yes, believe me. Robots have saved our bums from doing the hard work, yet again.

As businesses and companies need efficient data streamlining, artificial intelligence is the go-to guy.

AI provides real-time data for databases, and large-scale data storage solutions and models consumer behaviours for businesses. It can save us time and resources when processing and implementing data techniques.

But AI does more. By analysing and extracting patterns and insights from large data sets, you can predict trends and market gaps. 

According to Gartner, 75% of businesses will shift from trialling new ideas through small-scale manual implementations to trailing ideas with AI, driving a 5X increase in streamable data and better analytics infrastructures. 

But the reality is, that you do not need or can use AI in every scenario. Sometimes data processing requires a more thorough evaluation and can’t be done by simply letting it run through the system. Because each type of data can pose its unique problem, it involves a certain extent of manual processing to make it usable. And this means spending hours processing data, both strenuous and time-consuming.

The takeaway is, that AI can do most of the data processing. And, that is good news for us.

You Can Sell Data

It is official. We can now buy and sell data. 

I mean, who would have thought?

Data marketplaces and exchanges are online platforms where you can consolidate third-party data offerings. These marketplaces are a convenient way to market, manage and sell data and houses different types of data for each market.

Data marketplaces are becoming so popular that by 2022, 35% of large organizations will either be sellers or buyers of data through online marketplaces. Up from 25% in 2020. 

Although new marketplaces can feel overwhelming, it offers direct communication between third-party data providers and businesses looking to improve their vision. For small businesses, data marketplaces are a great way to build partnerships, get a rewarding insight into market demands and build consumer trust. As a result, enterprises can establish a positive reputation and take advantage of what’s trending.  

Cloud Is Essential For Data And Analytics

In the field of data and analytics, cloud computing is a must. 

Cloud computing refers to storing and accessing data over the internet through the delivery of various technology services. 

Without cloud computing, companies would not be able to access computing services like databases, servers, software, and AI, preventing efficiency in operations, demanding more time, and increasing the need for excessive maintenance regarding applications.

Cloud offers many advantages for businesses like pay-as-you-go pricing, scalability, reliability, speed, performance, and other benefits. With Cloud, you only need to pay for resources when you use them instead of considering capital expenses like buying software and hardware. And this all relates to data analytics because more resources and computing power makes it easier to deploy and facilitate data solutions. 

In data and analytics, the use of the Cloud is becoming more standard, as data analysts need scalable technology to keep up with data growth. Clouds’ easy-to-use infrastructure allows the ability to handle powerful algorithms with ease and drive analysis through various tools. And it does this anywhere and everywhere, on any device.

Where data and analytics go, Cloud will always be one step ahead.

X Analytics Makes Data Processing Easier

X analytics is quite literally the perfect case of algebra. You solve the equation by subbing in X. But what exactly are X and the equation?

The term refers to techniques that can run any analytics type based on data, regardless of what format that data is in or where you found it. Where X is the data variable, and the equation is the output of the analytics result. 

X analytics finds solutions to countless questions and makes data analytics more convenient. With the assisted use of AI, the X analytics method is a powerhouse for evaluating all types of data like research papers and news sources. 

Using X analytics during the pandemic meant many public health experts could access clinical trials data and evidential sources. Allowing them to streamline important information for the public, such as finding new treatment options and identifying preventive measures to stop the spread of the disease. 

Data Is Subject To Human Error

In a perfect world, data would be all sunshine and rainbows – unfortunately, our world is not so perfect.

The numbers in your excel spreadsheet came from data pulled through large databases and analysed through various analytics software. But where exactly did this data originate?

From us – from untrained data surveyors who have no idea what they’re doing. 

In many cases, the reason for inaccurate data is entering the wrong data in the first place. After all, all it takes is one slip of the key or pressing down on the wrong numbers to alter and un-neutralize data.

And human errors can not only impose vulnerabilities in networks but leak confidential information. And this can effectively damage the reputation of companies that customers trust.

According to a study by IBM, human error was the leading cause of 95% of cyber security breaches. In many companies, data errors can cause substantial problems like Phantom Inventory, impacting sales, decision making, and creating potential losses. According to many studies, 40 to 50 % of store items have some level of phantom inventory. 

So, although human errors may seem unimportant, it’s necessary to consider ways to minimize them to offer greater data quality. 

Cleaning And Preparing Data Is Inevitable

In data analytics, the belief that data is readily available to use is becoming more common. Contrary to this, businesses, companies, and data analysts will need to spend a great deal of time cleaning and processing data. 

And for newcomers to the data analytics industry, it’s a brick to the face.

There are all these complex machine learning methods. Why do we need to manually clean and prepare data?

Because data and analytics is all about understanding data sets and obtaining insights to help solve problems. As humans, we are capable of analysing things beyond a surface level. Machines can only do so much. Algorithms are reliant on certain factors like patterns; we are not.

If this is not your expectation, maybe it’s time to consider why you work in data science.

Presentation Is Key

What good is it to have data but not be able to present it properly?

In data and analytics, presentation software like PowerPoint and Google Slides offer ways to communicate data findings clearly and effectively to the end-user. 

Because in a business, company, or organization, stakeholders and executives need to understand the data before analysing it. With easy-to-follow presentations that include infographics and visuals, you can streamline findings and facets of data without boring the heck out of your audience.

Data needs to be presentable because our world revolves around complex information. So, it’s easy to get lost. 

Presentable data allows people to understand content clearly, sustaining their interests and effectively helping dissect complex data sets.