Big Data Analytics Software Trends
Big data analytics software is specially built to help your company gain insights from the massive sea of data you’re probably generating every day.
Web data, app activity, delivery times, and daily sales are just a few of the data sources that can generate millions of data points in short order. Business analytics without specialized software is difficult enough on small to medium datasets; without good software for big data analytics, you can completely forget about scaling up your analytics to bigger datasets.
Tools for big data help you tap into the insights from large datasets to give your company a competitive edge.
Why use big data analytics software?
Experts often differentiate “big data” from traditional datasets you’d use in business analytics and business intelligence using the five Vs: volume, variety, velocity, value, and veracity.
Big data means, of course, much bigger datasets, which imposes some technical limitations on your business analytics if you aren’t using specialized big data tools. Microsoft Excel, for example, long the favorite of old-school business analysts, can’t even open spreadsheets with more than about one million rows. Big datasets may have hundreds of millions of individual data records.
Big data analytics software is specially built for handling the massive size of big datasets, and the high rate at which they come in, and is also well-equipped to deal with data of many different types—the second and third of the five Vs, variety.
The data you generate may include website logins stored in a table, user interactions with customer service stored as raw text, and time-series data on daily sales over the past several years. A good big data analytics suite will help you both manage and use these data, even when you have heterogeneous types of data that are being generated very rapidly.
Another key advantage that big data analytics software offer over traditional analytics software is in its ability to deal with data anomalies and “dirty” or messy data. Ensuring the veracity of your data—how well it actually represents the underlying information you are trying to understand—is essential for getting good insights into your business.
Big data tools can help automate data cleaning, taking some of the workload off your data engineers, data scientists, and business analysts. These tools can also flag anomalies, which can alert you to problems in your data processing pipeline or problems with your products that are generating your data.
The final, and the whole point of using big data in the first place, is value: using data that provides useful information to better inform your decisions, ultimately enabling you to deliver better products and services to your customers.
Some of the biggest success stories today, like Google Search, the iPhone, Netflix, and Uber, all rely on big data analytics to deliver better value than their competitors. In each of these cases, it’s easy to see how employing big data can help: with the right data insights, the results are better search results, movie recommendations, and map navigation, to name just a few. How can your business improve on the value you deliver to your customers using data you already have?
Big data tools help you put all of your data in one place. Especially as the size of your business increases, it can be increasingly difficult to keep track of all of the different places data gets generated in all of the different divisions and product lines of your company.
One of the overarching philosophies of big data analytics is keeping all of your data in one, unified database. Otherwise, you can end up with chaotic and confusing analytics results.
In a 2013 article, the Harvard Business Review recounts the story of a CEO whose company had lost $300 million in the previous year who asked each division to produce a report on the division’s performance. Because each division was storing, processing, and analyzing their data differently, each division claimed to be operating at a profit (1)!
The problem here was a lack of one unified database, and the lack of big data tools to access this database.
Of course, you don’t keep all of your data in one physical place; one of the other major advantages of big data analytics software is that it makes it easy to access data that is stored and backed up in the cloud, across multiple servers that are organized into one logical database.
As the size of your database grows, so too does its vulnerability to data loss, hence the need for more robust database practices to keep your data safe.
Who uses big data analytics software?
The first people who need to start using big data analytics software are the data engineers and data analysts who actually get the raw data your company generates into a usable form. Making sure that your big data system has all of the data you’re generating is the first step to success.
In many cases, companies are sitting on huge swaths of valuable data they haven’t put to good use, often because of a lack of a proper set of big data tools to analyze the data. Your data engineers and data analysts may need to work with your IT team, especially if you are generating large volumes of data from your website. Moving huge datasets into a big data analytics suite might involve some IT dirty work, because of the size of the datasets and how they need to be stored in your servers.
Your IT team is also critical for ensuring that the size of your data continues to grow: keeping a steady stream of real-time data is essential if you want to capitalize on fast-changing targets like product demand.
Once you have usable data coming in on a regular basis, your data scientists and your business analysts will be the primary users of your big data analytics software. They’re the ones responsible for asking questions and getting answers from your company’s data, and communicating the insights they find.
Usually, data science and business analytics or intelligence team leads will be supervised by your chief information officer, or CIO, but some larger companies are now appointing a specific CDO: chief data officer.
In either case, it’s important to remember that the big data analytics process doesn’t end when the analyst has the results; the process ends when the correct insights have been communicated to the right people.
Because the ultimate goal of big data analytics is to deliver value to customers, that almost always means getting your sales, marketing, and product development teams involved, at least with evaluating the results of your data analysis.
Getting the feedback and expertise of your sales, marketing, and product teams is a huge help, because their knowledge can guide the direction of your big data analytics projects. With large datasets, there’s a potentially infinite set of questions you can ask—but only a small set of these questions lead to useful insights.
Once your big data analysis tools are in place, make sure the focus remains on using these tools to improve your business by delivering more value to your customers.
Big data analytics software should be able to handle a wide variety of data types. Traditional business analytics takes place in the context of spreadsheets and tables, where data fit nicely into rows and columns.
One of the defining features of big data is moving beyond simple datasets of numbers into a much bigger world of possible data: you could have raw unstructured text, like customer reviews, photos, audio, time-series data, sensor data, spatial and map data, and more. Good tools for big data will allow you to use many different kinds of data for your analysis projects.
Tools for data can allow you to join internal data with externally purchased data. Along with the rise of big data, there has been a rise of data brokers who sell data on purchasing preferences, marital status, educational attainment, credit history, and more.
By themselves, a giant dataset with this kind of information is not particularly useful, but when you can combine it with internal data you have, like social media interactions with your brand, purchasing history, geographic location, and more, you can get some powerful insights into the desires and preferences of your customers.
Using big data tools, you can join internally sourced and externally sourced datasets to augment the data you already have.
Big data analytics software can range from simple, easy interfaces to powerful but difficult to learn scripting suites. Within the field of big data analytics, there is a huge range of strategies for dealing with the complexity of big data.
Some companies (usually tech companies) hire legions of PhD-level statisticians and computer scientists who build their own big data tools from the ground up using tools like Hadoop, Spark, R, and Python. At the other end of the spectrum, some companies instead turn to simple point-and-click dashboards that are provided by tools like Salesforce Einstein.
While there’s still considerable expertise even with these more user-friendly big data tools, the biggest decision you need to make is what level of complexity you expect your big data team to work with on a day to day basis. Enterprise-oriented programming suites like MATLAB cater to companies that want to write their own scripts.
SAS, Oracle, Splunk, and others fall in the middle of the spectrum, and at the other end, you have Salesforce Einstein and Tableau, which are build more squarely from business analytics roots and are easier to use and translate into dashboards and graphs, but with the potential downside of losing some flexibility in what you can do with your data.
Good big data analysis tools will support Restful APIs. Most big companies have data being generated across a variety of sources from within their company, and these sources aren’t likely to be running the same software.
You may want to combine sales data, marketing data, data exported from your CRM system, and social media interaction data—to support integrating different data sources like this, look for big data analytics software that offers support for what are called Restful APIs.
REST stands for Representational State Transfer, which is a universal framework for how data is exchanged between different software systems. API, in turn, stands for application programming interface, which enables one software application to “talk” to another.
If your company’s data generating sources feature these so-called Restful APIs, it’s far better to leverage this ability using your big data analytics software package. It will be much easier to filter, select, and analyze the data you want, and it will be much easier to transfer the data you need into one unified database.
Make sure you include data visualization in the set of big data tools your company uses. Big data analytics often involves building very complex models which are hard for non-statisticians or non-data scientists to interpret.
While much of the focus with big data analytics software focuses on tools for manipulating your data, don’t forget to provide your team with tools that enable excellent data visualization: this means the ability to create plots and dashboards to monitor the insights gleaned from your company’s data.
Tools like Plotly, R, and Tableau all enable excellent data visualization, which is the best way to ensure that the message from your data analysis gets conveyed effectively.
Higher-end big data analytics tools may enable you to use unstructured data more effectively. One area with fast-paced progress in data analytics and business intelligence is the use of “unstructured” data like raw text, customer reviews, and emails and other documents.
While processing this kind of data is still an area of active research, some of the higher-end big data analytics tools have started offering the ability to detect customer sentiments (positive, negative, satisfied, unsatisfied, etc.) from raw, unstructured text, or to identify key phrases and predict customer actions based on the same kind of unstructured data.
If this kind of analysis is important to you, make sure you choose a set of big data analytics tools that support storing, processing, and analyzing unstructured, natural language data.
Q: What separates big data analytics from regular data analytics?
A: The defining characteristic of big data is that the entire dataset is too big to fit into your analytics software (or your computer) all at once. Depending on who you ask, “too big” might range from only a million rows of data to 20 million or so.
If you routinely find yourself staring at data that’s got millions of observations, though, you are definitely crossing into the realm of big data analytics. Big data offers the opportunity for more sophisticated analysis techniques, but also poses challenges for straightforward analysis. Big data analytics software helps add a level of abstraction, so you don’t have to load and operate on the entire dataset all at once.
Q: How do you use big data for marketing?
A: For marketing, the most helpful insights you can get from big data are predictions on customer preferences, and predictions on customer purchasing tendencies.
Big data gives you a huge leg up compared to generic marketing email blasts: if you can identify the users who are most likely to make a purchase, and set them up with a custom marketing campaign that recommends the products they are most likely to buy, you can see huge gains in your click-through rates and your conversion rates.
To use big data in this way, make sure you have both “input” and “output” data on your customers: who are they, what do they like, and what do they ultimately buy? If you have this data in-hand, any competent data scientist should be able to build a predictive model using your big data tools to improve your marketing campaigns.
Q: Is big data the same thing as data science?
A: Big data often goes hand in hand with data science, but they aren’t synonymous—data scientists often work with big data, but they might find themselves working with small datasets too. Likewise, if you have a huge dataset that is not particularly complex, it may not be that interesting to a data scientist; you might pass it off to a business analyst instead.
At most large companies, though, data scientists spend most of their time working with big data. That’s because big datasets enable you to use complex machine learning and artificial intelligence algorithms that can improve the accuracy of your predictions, but that require huge amounts of data to properly develop.
Q: Do you have to use machine learning to analyze big data?
A: Machine learning is very popular for analyzing big data, because large datasets can unlock the full potential of complex, sophisticated machine learning algorithms.
However, it’s not strictly necessary: sometimes all you need to do to analyze big data is a simple statistical model, or even a plot that summarizes the most important trends in your data.
Occasionally, these simple tools can be more useful than a fancy machine learning model, especially when the fundamental business questions you are asking are straightforward.
Q: Can big data analytics help with fraud detection?
A: Fraud detection was one of the first applications of big data analytics tools, particularly a type of analysis called anomaly detection. Banks, for example, have huge databases of financial transactions, but only a tiny fraction of them are known to be fraudulent.
By using big data tools, banks can build models that detect deviations from the typical spending patterns of a particular customer, flagging unusual purchases for review by the fraud team. You can use a similar strategy for your business by building a large dataset of genuine transactions, and at least a small number of fraudulent transactions.
Even with only a few cases of fraud, it’s possible to build algorithms that can flag them, assuming you have enough data to model the typical patterns seen in genuine transactions.
Q: Can you use big data analytics tools with protected health information?
A: Yes, big data analytics is very popular with health insurance companies, hospitals, and doctor’s offices, but you need to take some extra steps to make sure your system is compliant with regulations that govern protected health information, or PHI.
The key phrase to look for is “HIPAA Compliant,” which refers to a US federal law that governs protected health information. Making sure you set up a big data system that is HIPAA compliant is important, because your company could be held legally liable if you don’t appropriately follow the regulations.
Q: Do you need to be able to program to analyze big data?
A: A lot of big data analysis is built around programming, whether that is SQL queries, scripts for R and Python, or setting up APIs to transfer data.
However, if you’ve already got a team that manages your data and stores it in a data warehouse, there are several user-friendly tools that make it possible to analyze big data without programming abilities.
Salesforce Einstein and Tableau are just two examples of tools you can use to analyze big data without any real programming.
Q: Is data mining the same thing as data analysis?
A: Data mining is a particular type of data analysis that seeks to uncover new or interesting patterns in large amounts of data. Usually, data mining isn’t done with a specific question in mind, which puts it in contrast with traditional data analysis.
If you want to know which of your factories are producing the most products every day, that is a traditional data analysis question. If, on the other hand, you want to uncover patterns in your supply chain that affect factory output, that might fall more into the realm of data mining—you don’t have a specified, a priori question, and you may or may not find something new and useful.
Data mining tends to require bigger databases and more creative and sophisticated analysis, which is why it tends to be associated with big data.
Q: What is Kylo?
A: Organizations have tried to build complex, custom-engineered and Hadoop-enabled solutions in-house that often lack governance, security and quality control. These complex projects have become too costly and time consuming, resulting in business users losing interest and significant loss of the investment.
With these challenges in mind, Think Big has built Kylo™ on eight years of global expertise involving 200+ data lake projects in global banking, telecoms, retail and more. Kylo™ is a solutions platform for delivering data lakes on Hadoop and Spark. The benefits of Kylo™ include:
- Scale for the future: makes it easy to scale data lakes to large numbers of data feeds with a template approach and a visual interface to simplify creating and modifying them with security built-in
- Easy to use: includes an intuitive user interface for self-service data ingest and wrangling (no coding required!), allowing more IT professionals to access the data lake
- Metadata management: provides metadata tracking, allowing data stewards and data scientists to quickly catalog, discover and qualify data and understand the accuracy of data
- Operational monitoring: offers an operations dashboard for SLA tracking and feed monitoring
- Best-of-breed technology: built on modern open source frameworks such as Apache Spark and Apache NiFi
The Kylo™ journey is a three step process that addresses the key stages of data lake ingestion, transformation and discovery:
- Ingest: there are many tools that ingest batch data, but few that will work to ingest streaming or real-time data. Kylo™ supports a mixture of both.
- Prepare: using Kylo™, companies are able to pull apart and understand their data better. Kylo™ helps to cleanse data in order to improve its quality and to accredit data governance.
- Discover: once your data has been ingested, cleansed and installed in the data lake, your analysts and data scientists can begin to search and find what data is available to them. Kylo™ makes this data discovery simple, allowing users to build queries to access the data in order to build data products that support analysis.
Big data analytics software enables your company to capture, store, and analyze huge amounts of data for better insights into your customers, your products, and your services.
Using these tools can help you keep all of your data in one unified database, mine this database for new insights, and use these insights to deliver more value to your customers.
If your company is sitting on huge swaths of data but can’t put it to good use, you definitely need to start using big data tools to improve your business analytics abilities.