Data is all around us. We can find things to analyze everywhere and drive insight from this knowledge. This is why data science and big data analytics are such important and thriving fields today.
However, it is important to have the right team in place and developers that can use this knowledge for future product design. This is why hiring the right software development services can make a huge difference in making sure this data is not wasted.
The right team will be able to work with data scientists or data metrics directly and come up with ideas for products and services as well as make sure existing software is optimal.
Take full advantage of Big Data in Software Development
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Categories of Data Defined
Big data analytics look at large quantities of data collected constantly over a long period of time. Many servers are used to collect and store this data within huge datasets. Estimates suggest that big data is collected in the exabyte (one billion gigabytes) range.
This includes transaction records, financial information, browsing habits, social media activity, and mobile device usage patterns (what time of the day do users tend to rely on mobile, what are the user’s demographics, etc.).
Data can be separated into two main categories. One category is called structured data and the other one is called unstructured data.
Structured data can easily be identified and put into rows and columns on a spreadsheet. This data could come from daily website visitors or the most common tools users rely on within an application. It is relatively easy to identify and measure.
Unstructured data is hard to quantify or easily explain. It could include social media content like postings or overall user behavior. It could also come from real-world examples analyzing customers from video and audio samples.
There is a huge amount of data out there for the taking. It is ready to be logged, input into databases, and analyzed by experts. However, logging the data into databases and using rows and columns to input it into a file system takes time.
This process of transforming raw data into usable data, ready to be analyzed for business intelligence, is called extract, transform, and load (ETL). It is the backbone of data warehousing and requires specific software, infrastructure, and professionals (IT teams) to accomplish.
Data Analytics and Software Development
Data science includes data scientists using a variety of tools and software to make sense of the huge quantities of data companies gather on a daily basis. They gather data about their customers, competitors, and the market as a whole.
These Companies and the data scientists they have on hand then use tools such as Apache Hadoop in order to find metrics and insight from huge data sets and drive better business intelligence to their brands. They then work with software developers to turn this data into meaningful software for their brands.
A way software developers also make use of these vast quantities of collected data is through predictive analysis. This is a method of making predictions about future events from data analysis. New methods of data mining are often considered, along with predictive modeling, statistics, machine learning, and AI to make this happen.
Software developers can implement predictive analysis techniques from data scientists and other experts or in some ways conduct it themselves in order to install the right software, prevent bugs in the future, prevent security issues from arising, and keep their software up to date.
Historical data is also used in the predictive analysis to make sure apps and services available on mobile have a good UX and succeed with a wide audience. Mobile patterns of payments, such as in-app purchases, for instance, can be compared to premium app prices to find the best results for a particular genre of software a company wants to launch on mobile.
Big Data and Web Development
This is why big data is so important for development. Developers can find what works best with different user groups based on metrics like age, demographics, income, or geographical location.
Big data analytics also allows for the web experience to be personalized and tailored to each user. There is a lot of data available on the web that tracks users and their behavior. This is done using cookies, geolocation services, forms users fill out voluntarily, and heatmap or fingerprint trackers along with other software.
Even though ad blockers and GDPR regulations have made data collecting more challenging, there is still a lot of data to be collected from users worldwide. Advertisement companies use this information for product placement ads while companies use it to create a more personalized web experience.
Content management systems and efficient big data management are needed for web content to be dynamic and able to address different users’ needs. Big data analytics can answer questions such as “What is the optimal mobile experience for particular services?
Or “What is the optimal UX experience for younger vs. professional users using the same software?” It is allowing companies to focus on smaller customer segments rather than customers in large masses.
Google offers companies and webmasters a lot of metrics and data in order to gauge their site visitors or customers better and cater content to them. Google Analytics has this data available for site creators and webmasters to access anytime.
Data and statistics such as what age range users typically consist of that visit a particular site are available. This data can help the webmaster or developers working on the site to improve future products to cater to this age group and demographic.
It can even be broken up into different time segments showing how many visitors the site had weekly and monthly since it was launched. However, not everyone has a Google profile set up, and this data is mostly collected from existing Google users.
Big data mining or collecting goes beyond this when analyzing user activities. It looks at insights outside of Google’s range and both structured and unstructured datasets. And it is software developers who will turn this analysis into useful products and services of the future.
Potential Bias in Big Data Analytics & How Developers Can Help
Data may seem binary from a layperson’s point of view. However, there are many intricacies when trying to analyze it and make sense of it all. This is where bias sometimes comes into play.
This objectivity may be lacking among some data scientists and this can be worrying for companies wanting to use big data for predictive analysis, productivity growth, or other tasks.
One way this happens is when incorrect assumptions are made before choosing what data to collect or focus on. This can come from poorly defined business domain objectives or wrong assumptions from domain experts.
Sometimes, collected data is useless or incomplete for the objective. It may be too large to analyze in a reasonable amount of time while leaving out data that is more informative.
Data scientists can also individually add bias to the mix. They can influence dataset analysis with their own preconceived notions from previous experiences with similar datasets. Or, they can pollute data with bias by developing too much reliance on pre-existing algorithms without examining validity for a particular use case.
Data sparsity is a problem that sometimes occurs due to big data bias. It means that those in charge of data analysis end up drawing conclusions before enough data is gathered and considered in the overall analysis.
This can hamper future software development and sometimes software developers can provide insight themselves or work alongside a data scientist closely to find the most unbiased results. Opening a feedback loop within the company is the way to go in overcoming such data biases.
Big data today is tied to a wide range of tools and systems algorithms. An example of algorithms relying on big data analytics comes in the form of machine learning systems.
Big Data and Automation Through AI
Some say that AI may actually overtake humans entirely and bypass the need for a data scientist to gather insight into analysis. This could save companies time, effort, and money. The insight could then drive software development in new directions and give suggestions on which tasks developers should focus on.
The human hand and the need for data scientists aren’t going away anytime soon, though. The human element is often needed for software developers to make sense of the data and realize the potential of certain insights or predictive analytics.
Even though AI systems may start to offer a more automated way to collect and analyze data, software developers still need to implement it into action. Keep in mind that both data mining and predictive analytics use algorithms and different software to find useful data and make sense of it.
Thus, software design is ingrained in this practice, to begin with. Developers also come in handy when finding solutions to the problems that the data identifies on a regular basis through big data analytics.
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