Data analytics has become one of the foundational scientific practices of the modern world. A digitally driven society produces massive amounts of information, both online and in other forms; for instance, electronic records of transactions and movements. In its raw, unfiltered state, this data is everywhere. But when it is processed, analyzed, and understood, it becomes extremely valuable.
Commercial firms, government agencies, the voluntary sector, and many other bodies actively seek out data and constantly look for new ways to generate more of it. But raw data is useless without knowledgeable human experts and the appropriate technology to analyze it effectively.
What is data analysis?
A branch of data science, alongside related fields like data mining and machine learning, data analysis takes unsorted, unstructured data sets and applies scientific, statistical methods to find correlations, trends, and patterns, generally using complex, powerful computer programs and algorithms. This process turns raw data into usable information.
The big players on the digital stage, from Facebook, Amazon, and Google to IBM and Microsoft, all employ data analysis on a vast scale in order to stay the best in their business and several steps ahead of their competition. Smaller companies also use data analysis because they know that the insights gained are vital to contemporary business practice.
The uses of data analysis
Data analysis allows institutions to make informed decisions about their direction. Properly analyzed, data can give an impressively accurate picture of current trends from which future trends can be extrapolated. But raw data needs to be parsed, manipulated, and processed in order to become useful. The tools, technologies, and programs used to achieve this are changing all the time as new, improved versions become available. However, certain models and systems remain the bedrock of modern digital data analysis.
At one end of the scale, data analysis provides the most penetrating and accurate form of market research, revealing and predicting customer tastes and behaviors. Commercial firms can use this information to better tailor their goods and services to the appropriate demographic. At the other end of the scale, data analytics can be used to develop new technologies, alter human behavior, and even predict future events with an unprecedented degree of accuracy, allowing institutions to plan for otherwise unforeseen contingencies.
A fast-growing sector
As the world becomes even more connected and data-driven, with 5G technology, the Internet of Things, virtual reality software, artificial intelligence, cloud computing, and other current and future trends, so data analysis is becoming equally essential and ubiquitous. Budding analysts from any background can take an SBU online business analytics master’s at St. Bonaventure University Online to learn about analytics business strategy, communication, technology and more. With a bachelor’s degree in any field, the course can be completed in as little as two years, with the option to customize it towards your ultimate career goals by focusing on finance, cybersecurity, marketing, or other areas.
What follows are a few of the key technologies used in this field.
- Text mining
Text mining uses natural language processing (NLP) to extract useful information from raw, unstructured data sets. Trends and relationships are identified and patterns extrapolated. One important way that data analysts use text mining is in sorting through large quantities of customer feedback for the commercial retail sector.
Taken individually, customer responses may all seem to say different things, so using them to determine what actions a company should take can be difficult. Text mining allows data analysts to uncover the primary concerns of customers and present the company with a clear plan.
- Streaming analytics
Most data analysis is conducted after the fact, using the masses of data collected over a given period and analyzing it extensively once the information has “cooled.” Streaming analytics involves engaging with the data while it is still “hot,” i.e., performing real-time analysis of information as it occurs. Data flow may be produced by streaming sources, like web traffic to a site, social media, or the information given out by equipment sensors.
Although the analysis produced this way can’t be as sophisticated and in-depth as that produced at a later date with more time to work on it, streaming analytics is extremely valuable for revealing trends, patterns, and relationships while they are happening and allowing for immediate action.
An example of streaming analytics is the software used by stock market traders to predict price changes and buy or sell faster than would be humanly possible. Healthcare providers, security services, supply chain networks, and remote technology, including automated vehicles, also use streaming analytics to make essential split-second decisions in the moment.
- Microsoft Excel spreadsheets
It might come as a surprise to see an Office program used by millions of ordinary people worldwide on this list, but Excel is also one of the most ubiquitous and essential pieces of software for data analysts. Its calculation features and graphing functions work well for small-scale data analysis, and Excel’s popularity and compatibility with other systems mean it’s an ideal way to share results.
The downside is that Excel is out of its depth with big data, and calculations lose accuracy on a larger scale. Nevertheless, the form creation tools, pivot tables for sorting and totaling data, and the SUMIF function to create value totals based on variable criteria are extremely useful for data analysts.
- Microsoft Power BI
Another commercial software program from Microsoft, this business analytics suite was originally an Excel plug-in but now functions as a stand-alone program set. The compatibility of Microsoft BI with Excel and a host of other widely used systems, including SQL and Google Analytics, remains one of the suite’s main draws. It’s an excellent tool in its own right for data visualization and predictive analytics, and for creating interactive visual reports and dashboards.
- Python
This open-source programming language is an essential tool for anyone working in computer coding, and data analysts are no exception. Versatile and easy to use, it has an vast range of libraries. Of particular interest to data analysts are NumPy and Pandas, which can be used to streamline complex tasks and implement or manipulate multiple data operations and objects. Panda uses a 2-D data frame into which data can be imported from sources like SQL, JSON, XLSX, and CSV.
Also useful are Beautiful Soup and Scrapy, for scraping data from the internet, and Matplotlib for data visualization and reporting. Because it’s so data-intensive, Python tends to be slower than most other programming languages, but it remains the go-to standard for programmers, computer scientists, and data analysts.
- R
Another open-source programming language, R is used for statistical analysis and data mining, meaning it’s much employed by data analysts. Unfortunately, R is even slower than Python, and is also harder to learn. But because it was designed with statistical and data analysis in mind, it’s often the best tool for the job.
Other popular programming languages used in data analytics include C, C++, and FORTRAN.
- Jupyter Notebook
If you need to share your work, particularly if it is code based, or create an effective, interactive presentation, then Jupyter Notebook is the tool to reach for. This open-source authoring software lets you combine live code, equations, visualizations, and narrative text in one interactive document. Designed with data analysts in mind, Jupyter Notebook supports over forty languages, including Python and R, and can be integrated with big data analysis tools like Apache Spark (see below).
- Apache Spark
This open-source data processing framework lets you distribute large jobs across multiple computers and uses RAM rather than local memory, allowing big data to be processed far quicker than would otherwise be possible. Apache Spark is used to develop machine learning models that are based on large amounts of data, and includes MLlib, a library of machine learning algorithms such as classification, regression, and clustering algorithms.
- Statistical Analysis System (SAS)
SAS is a commercially available statistical software suite that was originally developed in the 1960s. It’s evolved considerably since then and is widely used for business intelligence and customer profiling, as well as data mining and predictive modeling. One of its main attractions for business is that it’s easy to use by non-specialists and doesn’t require a high level of training.
- Tableau
Another program that doesn’t require intensive training or serious coding skills, Tableau is a data visualization tool that can be used to present large amounts of data in the form of dashboards and worksheets. Other appropriate software will need to be used for pre-processing and scripting before the data is imported into Tableau.
- KNIME
KNIME, or Konstanz Information Miner, is an open-source cloud-based data integration platform that, again, doesn’t require a huge amount of technical expertise to use. Data mining and machine learning are KNIME’s strengths, and it’s often employed to generate data workflows and to perform in-depth statistical analysis.
- The future of data analysis
The amount of data available is set to grow exponentially over the next few years. One of the main reasons for this is the imminent roll-out of the Internet of Things (IoT), with smart, connected devices in every home, workplace, public space, and even our cars. This means that data analysis will need to become even more sophisticated and pervasive in order to take full advantage of the tidal wave of raw data heading rapidly towards us.
- Artificial intelligence and machine learning
Artificial intelligence (AI) and machine learning (ML) are already widely deployed in data analysis and underpin the way the Internet of Things works. Models and programs using AI and ML can process large amounts of data and perform analyses based on multiple features and parameters.
While these models may seem to be doing data analysts’ jobs for them, in reality, they are essential tools enabling human scientists to process data faster and more efficiently than would otherwise be possible. They’re particularly useful in the commercial sector, where not every company can afford to employ dedicated data analysts. AI models serve as a starting point to extract value and usable insights from vast quantities of unstructured data.
- Synthetic data
As if there wasn’t enough real-world data to deal with, analysts are also increasingly working with synthetic data, generated by simulations and large-scale computer modeling. These artificially created data sets are less risky in terms of security and confidentiality, are easily harvested, and can be generated to preset, customized parameters so the data is of higher quality for the specific purpose intended. Synthetic data will mostly be used to train AI and ML models, preparing them to effectively process real-world data in the future.
- Conversational analytics
We’ve already touched on the use of NLP in text mining, but the technology is really coming into its own with the rise of voice-enabled devices in the home, like Siri, Alexa, and Amazon Echo. These devices are a key part of the Internet of Things, and it is anticipated that in the near future, most household objects will respond to the human voice and be controlled by voice instructions.
Along with this development will come greater opportunities for conversational analytics, including sentiment analysis, leading to enhanced social listening and increased personalization. These techniques will help to make voice-enabled devices and chatbots more responsive, realistic, and versatile. Security features will also be improved if devices can be programmed to recognize and only respond to the voices of predetermined parties, such as the homeowner and family members, and no-one else.
Data analysis is a technology-driven field responding to the needs of a digital, data-rich society. As the amount of data being generated increases, so will the demand for data analysts grow. These scientists will also need more powerful and sophisticated technology to allow them to do their job effectively.
Data analysis is not a new discipline, but it has become massively more complex and essential in the digital age. The sector will doubtless continue to expand, with new technologies and programs enabling the next generation of data analysts to come up with ever more penetrating and transformative insights.