Four Ways to Overcome Your Company’s Big Data

big data

As predictive analytics and big data become central to the success of modern marketing strategies, you may feel overwhelmed with the variety of new terms being tossed around. But with a big data revolution already in motion, you can begin implementing and understanding some of these tools to propel your business efforts even further.

Here, we’ll separate some commonly confused methods and tools to lay out the purposes and possibilities with each. For the three data essentials your company should be focusing on, check out our other post: What’s the Best Way to Visualize Your Data?

Business Intelligence

When you think of business intelligence (BI) you might think of simple data collection. But true business intelligence involves transforming data into useful guidance. Thankfully, BI is no longer a set of unknown tools existing only for mega-corporations who can afford it. Essentially, BI tools offer answers to big questions that matter through reports, KPIs, and trend-reviewing.

BI software allows companies to gather their data into one program, rather than juggling several lower-capability tools like Access and Excel. BI software typically stores data in warehouses, allowing for easier collaboration and collective decision-making within a company.

Download our eBook if you would like to learn how to implement business intelligence in your company.

Data Science

On the surface, data science may sound identical to business intelligence, but there are several differences – the most important being that BI provides data visually, while data science extracts data to gain more insight.

Using data science, you can uncover the significance of questions that you didn’t even think to ask. You might also discover rich information, like what kinds of content are most likely to go viral, ways to optimize emails or your rate of customer churn. Instead of being warehoused, data can be shared in real-time.

Data science opens up unlimited opportunities, as businesses can conduct more experimental research and delve into new markets of which they were previously unaware.

Predictive Analysis

Predictive analytics is a branch of data science. It can strengthen your ability to understand customers and tweak your decision-making to be more accurate. Companies can finally have a data-informed strategy for out-performing competitors.

The specific power of predictive analysis is that it gives companies insight into relationships and correlation – does A impact B, and how? While you may not prove causation, the simple finding of a correlation can guide strategic moves and ultimately boost your sales, audience size, and more.

Machine Learning

Many mistake machine learning (ML) for artificial intelligence. However, the purpose of ML is not to create technology with advanced cognition. The purpose is to streamline the process of solving certain business problems. Using mathematical and statistical algorithms, ML allows businesses to use data to fine-tune marketing campaigns for maximum effectiveness.

While it may seem that ML is a brand new function in digital marketing, examples of it can be found in many common places – Google’s “did you mean” capability that corrects spelling errors is just one example.

One factor you must keep in mind is that the quality of your data greatly impacts the usefulness of ML tools. Many predict that ML will enable a much higher capacity for “data storytelling,” answering many of the “why’s” that businesses couldn’t determine before.

While you may not grasp each and every concept in big data just yet, or know precisely how and when to utilize every tool, you can begin a slow immersion — one that will eventually lead you to harness more information, make better decisions and build a stronger business.

For more information on these methods and tools, simply reach out to us or visit our website.

Smart Data Series: An Introduction to Machine Learning

machine learning

To conclude our Smart Data Series, we are going to talk about machine learning. If you missed any of the topics in this series, you can visit them below:

Machine learning has turned the business software market upside down. Instead of users telling business software what to do, the software now tells the user what to do. Those who don’t quickly adopt this smarter, more predictive software risk falling behind.

What Is Machine Learning?

Traditional software strictly follows a set sequence. While outcomes might vary based on the input, the trigger for each outcome is preset within the code. Given the exact same inputs, the outcome will always be the same unless a human changes the code. Machine learning, on the other hand, automatically adapts its program to reach the desired outcome.

Consider the example of spam filtering. A traditional filter is programmed to look for specific keywords, email addresses or patterns. If it isn’t achieving the desired results, the programmer would need to manually adjust the filter.

A machine-learning spam filter might start with those initial parameters, but it would continually adapt. When a user flags a spam email that got through the filter or marks a flagged email as “not spam,” the machine-learning filter compares the characteristics of those emails and adjusts its parameters to try to get similar emails in the right place without user intervention.

While both a human and a machine can theoretically reach the same results, the machine can process large quantities of data more quickly and implement immediate changes without the need to wait for a software update. Additionally, while hard-coded software is generally one size fits all, machine learning can adapt to the preferences and habits of individual users.

Machine Learning and Data Science

Machine learning derives from data science, the practice of extracting knowledge from large sets of data. These large data sets can be used to train machine learning algorithms. In turn, machine learning has widened the possible range of applications of data science, as it increases the range of situations in which a machine can be used to draw conclusions from data. Algorithms can often analyze data much more quickly than even the most highly skilled human, speeding up many data analysis tasks.

Business Applications of Machine Learning

While the big data trend goes back decades, machine learning opens data analysis into new areas. Instead of big corporations having to make budget decisions about where to allocate teams of analysts and small companies being left out of the game, machine learning can be integrated into virtually any piece of software. Here are just a few examples of applications.

Inventory and Pricing

Retail stores and manufacturers are faced with constant decisions about how much inventory to order and when to reduce prices to avoid unsold inventory. Infinite factors can influence demand, including what nearby competitors do, the local economy, weather and demand for complementary items.

While pricing and inventory analysts have long built models to analyze these trends, machine learning can use past data to calculate infinite scenarios and build more accurate predictions.

Quality Control

A business’s reputation can be tarnished long before customer complaints reach its executives. Airbnb found this out after reports of discriminatory actions by its hosts surfaced.

Machine learning can look for small patterns and red flags that could indicate potential problems before the customer is even impacted. This allows companies to maintain high levels of quality and proactively deal with any problems that do arise.

Fraud Detection

Machine learning can also turn areas like consumer or employee fraud into issues that can be dealt with proactively rather than reactively. Transactions that fall outside of typical patterns can be flagged for manual review.

If the transaction turns out to be fraudulent, greater emphasis is placed on similar patterns in the future. If there was a non-fraud explanation, the software can adapt to avoid future false alarms without ignoring real frauds.

As machine learning continues to grow, the question will shift even more clearly from “How can you afford machine learning” to “How can you afford not to use machine learning?”

For more info on this topic, contact us today or visit our website.

Smart Data Series: An Introduction to Predictive Analysis

predictive analysis

Last week, we started our Smart Data Series. We gave you an introduction to data science and business intelligence. Well, you can’t talk about data science without talking about predictive analysis. So let’s dive right in.

Have you ever wished you could predict how much a customer would purchase in the future? Or maybe you wish you could figure out what advertisement to place on what publication? Predictive analytics may sound intimidating, but it’s really just a fancy term that means listening to data to make smarter business decisions.

Even if you’re already utilizing big data in some aspects of your business, you may be surprised to learn what else you can do with it. According to a VentureBeat report, “73 percent of marketing analytics reporting time is spent on evaluating the past and the present, while only 27 percent is spent on predicting and influencing the future.” This marks a huge, untapped opportunity for improvement among the majority of businesses today.

How It Works

If you’ve ever studied probability, you already have a rudimentary understanding of how predictive analysis works. These analytics tools collect data, analyze that data and construct models to predict the likelihood of certain events.

Then, you are given access to this information—typically via spreadsheet—without having to trudge through the difficult calculations yourself.

Other predictive analytics tools display data through integrated development environments or workflow models.

While predictive analytics tools were limited by data access issues in the past, advancements in technology are slowly bridging the gap and providing businesses with better resources. Relational databases are now used most often, as they allow for easier reorganization of data.

Some predictive analytics tools now operate from the cloud as well, giving businesses a chance to scale comfortably as their data size grows.

What You’ll Need

While your needs will depend largely on the platform you choose, there are a few things to consider and prepare for.

First, you’ll want to determine your data sources—where your data will come from. Businesses typically use up to five data sources from which predictions can be generated.

There are also unique languages with which your results can be interpreted. These include SQL, R, Java and more.

Ultimately, the most important factor to determine ahead of time is what areas you’d like to improve the most. Is your digital marketing strategy in need of a serious boost? Do you have only a minimal understanding of your customers’ needs and desires?

These questions will help you determine where you need additional knowledge and which data sources would be appropriate to use.

Opportunities Are Limitless

When considering how predictive analysis can benefit your business, the possibilities are virtually endless. There are several common ways predictive analysis is already being used in modern business—for example, calculating customer lifetime value (CLTV), or the total predicted profit from a single customer.

Another example is recommended product sections, which feature a personalized list of items one customer might be interested in (Amazon famously utilizes this tool).

Yet another incredibly helpful and common calculation is customer retention, which can empower businesses to target subsections of customers with tailored offers.

Some businesses employ analytics primarily for marketing and sales guidance. Data that separates customers based on their interactions with your website can reveal your best-bet leads—those you’ll want to reach out to ASAP.

Some businesses have been able to cut down their budget, generating quality leads with less effort thanks to these tools. Regardless of your reasons for employing predictive analytics, you can expect to increase efficiency, save money or streamline processes—or even all three.

If you want to learn more about how predictive analysis can grow your business, contact us today or visit our website.

Smart Data Series: An Introduction to Data Science

data science

Earlier this week, we gave you an introduction to business intelligence as part of our Smart Data Series. Today, let’s get into another big data topic: Data Science.

Big data is all around you. It’s online, in-store, on your phone, and is driving innovations in the business software market. Big data streams in from everywhere to help enterprises understand consumer behavior, make better business decisions and predict future actions more accurately than ever before.

Wired Science

As big data has grown, so has the need for big data scientists in the commercial world. The more sensors and algorithms there are that generate data all over the planet, the greater the need has become for professionals who can interpret and explain what that data suggests.

In short, data science today is really the discipline of turning raw numbers into business intelligence.

Data science as an academic specialty has been around for decades. In the 1970s, as computers shrank from room-sized monsters to desktop companions, data science was born from a combination of computer science, visualizations, advanced math and statistics.

3 Emerging Trends

Today three of the most significant emerging trends in data science involve predictive analysis, machine learning, and data mining.

Predictive Analysis

Forecasting and more accurate predictions of emerging trends have many applications, from finance to marketing. Intelligent software can model customer behavior and make reasonable conclusions about what they will want to do next, such as the Amazon recommendation engine.

It also refers to enterprise software like adaptive ERP systems that make changes to planning in real time and immensely simplify project management.

Machine Learning

There’s a limit to how much programming can do.

Machine learning allows computers to take it from there and program the next phase themselves. This technology has gotten a great deal of press as precursors to artificial intelligence, especially IBM’s Watson. Platforms that analyze data in new ways are already beginning to have a bigger impact on enterprise operations than the changes following the introduction of SQL databases two decades ago.

Look for many aspects of the business landscape to be disrupted suddenly as more businesses gain access to the massive compute powers of these new data analysis platforms.

Data Mining

Raw data doesn’t help anyone.

Data mining pulls useful knowledge out of that data, but until recently you had to be a data scientist to manage it.

New dashboards for data mining and simple tools like the single letter programming language R brings data mining to non-technical business professionals. This is part of the larger evolution of technology in society, which typically starts with formulas for experts and ends with a push-button interface for everyone.

Large companies are now democratizing data mining by embedding visualization tools into more familiar software packages.

Big Data Predictions

Companies that rely on big data for decision making have been 6 percent more profitable than more traditional firms. Data scientists can testify that even a small competitive advantage like this becomes decisive when compounded over time.

Over the next few years, this small change will drive major industry shakeups as IoT sensors proliferate and access to virtual data centers in the cloud becomes more affordable for smaller businesses.

The biggest hurdle now will be finding enough professionals with data science skills to analyze that data. This new business landscape will favor the companies that prepare themselves now to learn from and act on that flood of data. These companies will redefine their respective industries.

This year, the Harvard Business Review asked C-level execs to predict which industries would see the biggest disruptions due to data science in 2017. The top five were media, telecom, consumer financial, retail/technology (tied) and insurance.

Consider how vastly different the business landscape will look as top performers in these fields are upended. That will drive significant societal changes as people adapt to the new reality, just as smartphones and cloud-based apps ushered in a global mobile revolution.

Data science is on the cusp of bringing sweeping changes to the way people live and the way businesses operate in the very near future.

For more information on this topic, contact our team today or visit our website.

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Smart Data Series: An Introduction to Business Intelligence

business-intelligence

This month, we are focusing on smart data and how it makes an impact on your business.

We will cover topics on business intelligence (BI), data science, predictive analysis, and machine learning. Let’s start with our first topic: business intelligence.

Have you ever heard of business intelligence? What exactly is it? Sometimes referred to as BI, business intelligence can be defined different ways, but it basically means providing relevant, actionable information to you quickly in order for you to make better business decisions.

Actionable Information

To get this done, there has to be a way to collect, organize and deliver the data you need reliably and quickly. Certainly there is no shortage of data available. One study reports that more than 50,000 GB of data will be created every second by the year 2018.

What you need are tools to convert all the data at your disposal into insightful information you can actually use.

Grab Data Quickly

That is where business intelligence software comes in. There are a lot of factors that go into BI, including data analytics, performance management, data mining, predictive modeling, and other areas.

It’s useful to think of business intelligence as a convenience store. When you stop at a convenience store to pick up some bread, milk, and soda, you do not have to ask the clerk where to find them. There are sections and signs that clearly indicate where the different items are located. It is easy to quickly grab your items, organize them for purchase and be on your way.

Centralized Hub

To extend the analogy, business data is like the milk, bread, and soda — instead, the items might be sales forecasting, production data and regional manufacturing results.

Instead of looking in three different places for the information, business intelligence centralizes it all in one location so you can get a quick overview of the current state of your business. It takes the mountains of data that your accounting, sales, marketing, production and manufacturing systems generate and turns it into actionable information that you can manage on your own.

Just like at the convenience store, you do not need to ask to find what you need. You can quickly navigate through the data and pull the relevant information.

Easy to Understand

This saves you from trying to make sense of reams of confusing spreadsheets and reports. Now you can just ask your business intelligence software what you want to see, and it will deliver the information in an easy-to-understand visual representation that allows you to drill down for even further information if you desire.

This gives you many advantages: You can access the data at any time, find areas of strengths and act on them, shore up weaknesses in your operations and uncover new opportunities rapidly.

Real-World Examples

Here are a few examples of business intelligence being used in real-world situations.

Continental Airlines invested heavily in real-time BI and experienced a return on investment (ROI) of more than 1000 percent. The system allowed them to improve baggage handling, customer complaint management, ticket sales and booking procedures.

Hospital Corporation of America enhanced their business intelligence system to better track patient arrival times, average time spent with patients, turnaround time of exams and services, wait times for lab results and hospital bed vacancies.

Perry’s Ice Cream realized they were not gathering enough data about their guests and their buying patterns to grow their company to the next level. After implementing business intelligence software, they were able to see fine-grained information about each transaction and the type of customer behind each one.

Better Decisions Faster

No matter the industry you are in — from manufacturing to healthcare — business intelligence delivers actionable information in real time. With that data, you can make better decisions, and make them much faster. You will stay ahead of your competition and sell more products and services in an increasingly tough business environment.

For more information on this topic, download our eBook: How to Implement Business Intelligence in Your Company or contact us today!

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