Big Data Business Intelligence Data Analytics

How to Drive Growth & ROI with Marketing Analytics

What is Marketing Analytics?

Analytics, at its core, is the study of numbers. Numbers provide valuable data and insight into any given marketing strategy by making performance quantifiable. With marketing analytics specifically, the numbers help to track, identify, and understand user behavior. 

Understanding your audience is critical to making sound marketing decisions that deliver the best ROI and drive positive growth. 

In a simplistic breakdown, marketing analytics are intended to perform in two ways: 

1. Measure the effectiveness of marketing strategies and campaigns. 

2. Identify opportunities for improvement that will yield greater results. 

Why Do Marketing Analytics Matter?

Without marketing analytics, a business is operating blind. The marketing campaigns are merely pushed out to the world with little to no understanding of how strategies are landing with your audience. 

 Marketing analytics matter because: 

  • It makes the actions quantifiable. Whenever numbers are used, it provides concrete data for the marketing program. For example, it’s easy to notice that overall sales increased after a personalized content marketing campaign. Though, a more effective approach would be tracking the specific percentage (25% e.g.) of traffic that came from a blog published at a specific time (November, e.g.) and converted a specific number of leads (5% e.g.) for a specific product (holiday gift, e.g.).
  • It helps plan for future marketing. When you understand which tactics are working, you can strategically plan for future marketing. Not only does this help with planning marketing activities, but also overall budget allocation. 
  • It identifies the “why” of what did or didn’t work. After a marketing campaign or strategy has launched, the only way to adequately understand performance is with marketing analytics. The data can be drilled down to track individual messaging across a broad spectrum of outlets, ensuring no approach is wasted. For example, maybe your click-through rates from social media to your website are fantastic, but not converting to sales. With this information, you can focus your energy on shifting the homepage to reduce bounce backs.

How Can Marketing Analytics Drive Growth?

Ultimately, the information provided by marketing analytics is meant to drive growth and provide positive ROI. There are a few key ways that marketing analytics can help drive growth: 

Identify target audiences. One of the most brilliant things that marketing analytics can do for businesses is to segment audiences. The analytics can help identify and group users by:

  • Age
  • Gender
  • Geographic Location
  • Income Level

Even further, marketing analytics can identify subgroups or intersections in data sets. For example, a segment could be women, aged 30-45, in Tulsa, OK.

When grouping users together, data can be extracted surrounding how to best target those groups. Marketing analytics can also reveal new groups that are worth targeting. For example, perhaps the target audience is believed to be women aged 30-45 years old, but you find that certain marketing tactics are delivering positive results in teenage boys aged 16-20 years old. Having that information is powerful when driving growth. 

Predict future user trends. Predictive analytics compile past trends and historical data to help determine how users will behave in the future. This can help plan marketing strategies to align with certain seasonal behaviors. For example, sales of certain products are higher during the summer. By targeting audiences in the discovery phase in spring yields the most beneficial results. 

Eliminate what doesn’t work. One of the best ways to reap rewards with marketing is to eliminate the tactics and strategies that aren’t working. The less time and money spent on fruitless endeavors, the more growth can happen. 

Which Marketing Analytics Deliver the Best ROI?

The answer to this question will be different for each business. However, some general items will ensure marketing analytics deliver the best ROI. 

1. High-Quality Data

The power is in the numbers. Data that is quality both in scope and extraction is so important to delivering the best marketing ROI. The best quality data will be: 

  • Current
  • Consistent 
  • Precise 
  • Accurate
  • Relevant 

2. Combine Past, Present, and Future Data

To achieve a comprehensive overview of marketing analytics, all data should be considered. The past provides insight into user behavior and trends, while the present focuses on relevant current climates. The future is a prediction based on past and present trends, therefore eyes must also be turned toward the future to properly steer marketing strategies.

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5 Common Challenges of Data Integration (And How to Overcome Them)

Big data is a giant industry that generates billions in annual profits. By extension, data integration is an essential process in which every company should invest. Businesses that leverage available data enjoy exponential gains.

What is Data Integration?

Data integration is the process of gathering and merging information from various sources into one system. The goal is to direct all information into a central location, which requires:

  • On-boarding the data
  • Cleansing the information
  • ETL mapping
  • Transforming and depositing individual data pieces

Five Common Data Integration Problems

Getting a data integration process purring like a finely tuned Ferrari takes expertise, and the people running your system should intimately understand the five most common problems in an informational pipeline.

#1: Variable Data From Disparate Sources

Every nanosecond, countless bytes of data are moving rapidly around the ether — and uniformity isn’t a requirement. As a result, the informational gateway of any database or warehouse is a bit chaotic. Before data can be released into the system, it needs to be checked in, cleaned, and properly dressed.

#2: The Data/Security Conundrum

One of the most challenging aspects of maintaining a high-functioning data pipeline is determining the perfect balance between access and security. Making all files available to everyone isn’t wise. However, the people who need it should have it. When departments are siloed and have access to different data, inefficiencies frequently arise. 

#3: Low-Quality Information

A database is only as good as its data. If junk goes in, then waste comes out. Preventing your system from turning into an informational landfill requires scrubbing your data sets of dreck.

#4: Bad Integration Software

Even if your data shines like the top of the Chrysler Building, clunky data integration software can cause significant issues. For example, are you deploying trigger-based solutions that don’t account for helpful historical data?

#5: Too Much Useless Data

When collected thoughtfully and integrated seamlessly, data is incredibly valuable. But data hoarding is a resource succubus. Think about the homes of hoarders. Often, there’s so much garbage lying around that it’s impossible to find the “good” stuff. The same logic applies to databases and warehouses.

What Are Standard Data Integration Best Practices?

Ensuring a business doesn’t fall victim to the five pitfalls of data integration requires strict protocols and constant maintenance. Standard best practices include:

  • Surveillance: Before accepting a new data source, due diligence is key! Vet third-party vendors to ensure their data is legitimate.
  • Cleaning: When information first hits the pipeline, it should be scrubbed of duplicates and scanned for invalid data.
  • Document and Distribute: Invest in database documentation! Too many companies skip this step, and their informational pipelines crumble within months.
  • Back it Up: The world is a chaotic place. Anomalies happen all the time — as do mistakes. So back up data in the event of mishaps.
  • Get Help: Enlist the help of data integration experts to ensure proper software setups and protocol standards.

Data Integration Expertise and Assistance

Is your business leveraging its data? Is your informational pipeline making money or wasting it? If you can’t answer these questions confidently and want to explore options, reach out to Inzata Analytics. Our team of data integration experts can do a 360-degree interrogation of your current setup, identify weak links, and outline solutions that will allow you to move forward more productively and profitably.

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Top 5 Growing Data and Analytics Trends for 2021

What Are the Top 5 Growing Data and Analytics Trends for 2021?

Today, data and analytics are constantly evolving the way we do business. Companies are becoming heavily reliant on historical big data to create a foundation for decisions, from driving cost efficiency to determining new strategic directions. These decisions are more important than ever, especially when businesses are battling to stay competitive amongst the digital frontier.

To keep the competitive edge in the turmoil of a global economic crisis, more companies are taking the lead to be proactive in data analytics. The traditional AI techniques of being driven by big data are rapidly transitioning to the class of analytics based upon smaller yet more varied data techniques. Automation technology is the solution to this rapidly growing, complex problem.

Here are five of the biggest analytics trends to look out for in 2021.

1. Why Analytic Automation with Advanced and Scalable AI

With the demise of “big data” and the pivot to “small data” analytical techniques, AI systems will be required to do more with less. Advanced AI has the unique ability to analyze complex data sets and quickly detect trends and patterns that would be challenging or easily overlooked by the human eye. Analytics automation provides the opportunity for corporate analysts to focus more on high-value targets to drive top-line growth. Intelligent automation is the “engine” for today’s complex and challenging data-driven decision-making.

2. How Xops Delivers Automated Analytics for the Enterprise 

Xops has an objective to increase efficiencies with an economy of scale by deploying DevOps best practices. The end goal is to reduce cross duplication of technology automation processes while maintaining high levels of reliability, accuracy, and reusability. The key components of Xops (data, machine learning, model, platform) scale with DevOps best practices to maximize the value of analytical information. Xops promise to automate and accelerate the collection, collation, and identification of data elements, ultimately helping organizations keep their competitive edge.

3. What Dashboard Automation Promises for the Organization

The rapid movement to deploy data automation solutions that deliver insightful user-created data has tremendous implications. Analytical data traditionally would have to be delivered by IT or a data expert within the organization. The promise of analytical data generated on-demand by anyone ranging from marketing, human resources, or even finance will shift organizational agility, delivering insights faster and more effectively to the company as a whole. The impact on an organization from decentralized to on-demand data delivery can be dramatic.

4. Why Cloud Services Are Rapidly Growing for Analytical Data

With the advent of increasingly complex and larger data sets along with their intrinsic values, cloud services are rapidly becoming the repository of choice. Data is stored outside an organization on remote, secure servers. Extremely valuable information is better protected and in case of a disaster, data can be recovered much more efficiently. Scalability for the enterprise is more fluid with the cloud hosting services.

5. Why Data Analytics Has Become a Core Business

Data analytics has transitioned from being a secondary support function to mission-critical for an organization. The buy-in for data analytics has widespread support from the board room and the C-suite for its tremendous potential. The positioning of data analytics as a core business function, though, does not come without cost. Often, businesses may underestimate the complexity of data collection and analytics, missing valuable opportunities. Many times, partnering with AI-powered tools such as Inzata will immediately shorten the ramp-up time to deliver valuable data assets.


These data and analytics trends are being driven by analytics automation platforms. Technology is rapidly advancing, and organizations embracing it first will enjoy a competitive edge. Previously, a few gatekeepers generated the data analytics and insights based upon specific data requests from corporate analysts. With the trend for insight generation being pushed down to the individual, data analytics becomes available to all. The ability to create insights on-demand by the end-user will inevitably lead to a leap in corporate strategic planning, decision-making, and the ability to maintain a competitive edge.

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The Costly Compound Effect of Bad Data in Your Warehouse

Bad data can be seen as kryptonite to a company’s bottom line. Like a super spreader, it sneaks in, replicates, and corrodes your informational warehouse like waves on clay. And when that happens, trust is compromised, which can lead to additional risks and possible mishaps. After all, a company’s reputation and insight accuracy deeply impact its bottom line.

What is a Data Warehouse?

Data warehousing technology allows businesses to aggregate data and store loads of information about sales, customers, and internal operations. Typically, data warehouses are significantly larger than databases, hold historical data, and cull information from multiple sources.

If you’re interested in learning more about data warehouses, try reading: Why We Build Data Warehouses

Why is Data Warehousing Important to Your Bottom Line?

In today’s highly personalized digital marketing environment, data warehousing is a priority for many corporations and organizations. Although data warehouses don’t produce direct profits, the information and insights they facilitate act as beacons for corporate and industry trajectories. For some businesses, informational warehouses provide the data fuel needed to populate their apps and customer management systems.

What is Good Data?

A data warehouse is only as good as the information in it, which raises the question: what constitutes good data?

Informational integrity is tied to seven key pillars:

  1. Fitness: Is the data moving through the pipeline in a way that makes it accessible for its intended use?
  2. Lineage: From where is the info coming, and is it arriving at the proper locations?
  3. Governance: Who has access to the data throughout the pipeline? Who controls it?
  4. Stability: Is the data accurate?
  5. Freshness: Did it arrive on time?
  6. Completeness: Did everything that was supposed to arrive land?
  7. Accuracy: Is the information accurate?

Early Detection Saves Time and Money

The longer it takes to find a data pipeline issue, the more problems it creates — and the more it costs to fix. That’s why early detection is vital.

Data errors are like burrowing viruses. They sneak in and keep a low profile while multiplying and festering. Then one day, seemingly out of the blue, the error rears its ugly head and causes chaos. If you’re lucky, the problems stay internal. If you’re unlucky, the error has a catastrophic downstream effect that can erode confidence in your product or service. 

Examples: The Costly Compound Effect of Data Warehouse Errors

We’ve established that data warehouse errors are no-good, horrible, costly catastrophes. But why?

Upstream Data Provider Nightmare

Imagine if other companies rely on your data to fuel their apps, marketing campaigns, or logistics networks. A mistake that manifests from your camp could have a disastrous domino effect that leads to a client-shedding reputation crisis.

Late-Arriving Data

Late-arriving data is another nightmare if other companies rely on your data. Think of it as a flight schedule. If one plane arrives late, it backs up every other flight that day and may force cancellations to get the system back on track.

Understanding Leading Indicators of Data Warehousing Issues

Leading indicators signal that bad data has weaseled its way into a data pipeline. However, built-in status alerts may not always work. For example, it’s possible to receive a 200 success response from an API built on the HTTPS protocol since the check only applies to the connection, not the data transfer. Intrinsically, it’s essential to understand the leading error indicators.

Catch data pipeline leading error indicators by:

  • Setting up baselines
  • Establishing data checkpoints
  • Tracking data lineage
  • Taking metric measurements

Maintaining a healthy data warehouse is of vital importance, especially if other businesses rely on your services. Working with data warehousing solutions is often the best option in terms of cost optimization, speed, and overall performance. They have the skills, tools, and institutional knowledge to ensure everything runs smoothly.

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The Real Competitive Advantage of Real-Time Analytics

Information is power, or so the saying goes. Yet, that power rests in large part on how recently that information was collected. Let’s say you want to buy a house, you take the tour and like what you see. Before you make your final decision, though, you want to see a home inspection report. Do you want to base your decision on a home inspection report from six months ago or the one the home inspector will finish an hour from now? How about an inspection report from one month ago? One week? 

Information is constantly changing, anything could have changed within the past few weeks alone. To make effective and accurate decisions, you need to be working with the most current information. That desire rests at the heart of real-time analytics and the competitive advantage it offers.

What is Real-Time Analytics?

Businesses are inundated with data from countless sources. They get data directly from customers, aggregate data from social media, traffic data from websites, and even from marketing tool suites. Real-time analytics takes all of that data, processes it, and provides up-to-the-moment results. 

The kind of results depends on the analytics software you use and the settings involved. For example, some businesses prefer that the software provide answers only when queried on something specific. Others prefer that the software offer real-time alerts or trigger real-time actions for certain pre-set results.

Why Does it Give You a Competitive Advantage?

No business can know with certainty what data its competitors possess. You also can’t know with certainty how soon they’ll analyze that data and put it to work. Leveraging that real-time information lets you adjust tactics, change orders, or even create promotions in response to new trends before your competitors do. 

That lets you collect on the immediate benefits of reduced waste, better marketing, and an uptick in revenue. It also helps solidify your business as being on top of what is happening in the world. Customers like businesses that either predict or seem in close tune with the market. Every time you seem on top of your game, you cement your business as the one people should turn to first. 

What is the Cost of Working with Outdated Data?

So, what are the pitfalls of working without outdated data? For some functions, such as forecasting, data that is a little out of date probably won’t change the results by a significant amount. 

For any business that must respond quickly to rapidly changing trends, the cost can prove to be high. Let’s say a trend highlighting the value in some otherwise obscure IT function is rapidly developing in a specific industry. If you run a managed IT service company, recognizing the trend quickly lets you update your service offerings, set pricing, and shift your targeting strategy. If you don’t catch that trend quickly, you can lose out on potential revenue from new customers and expanded services for old customers. 

Use Cases

One of the most obvious use cases for real-time analytics is monitoring your IT infrastructure. Immediately you are able to gain visibility into the capacity, availability, and performance of your infrastructure investments. This lets you respond to any issues as soon as they arise. Getting the information tomorrow won’t be any help to your service levels. 

Another common use case is for digital marketing efforts. Let’s say that you offer a rewards program for customers. When they are shopping online or in an app, it’s a golden opportunity for some personalized marketing based on their previous purchase history. Real-time analytics can alert you or your automated marketing system that the customer is browsing the store. That lets you deliver customized discounts, coupons, or personalized promotions when someone is most likely to buy.

Real-time analytics is a powerful tool for carving out a competitive advantage. It helps keep your company at the forefront of changing trends. It also helps your business adapt faster when the unexpected happens. In turn, you reap short-term as well as long-term benefits in terms of cost-savings, revenue boosts, and customer conversions.

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7 Effective Tips to Secure Your Cloud Data

It’s been said that we “live in the cloud” — the digital stratosphere where images, videos, and files orbit. According to recent counts, the often mentioned but elusive digital ether holds 40 trillion gigabytes of data — or 40 zettabytes, which begs the question: Is it safe out there on the digital frontier?

The answer depends on precautions taken.

What Is Cloud Data?

In the old days, businesses maintained in-house computers to catalog files, and individuals typically stored their documents on desktops, laptops, and portable hard drives. But today, the “cloud” — a network of servers that can be accessed via the internet — is the primary digital storage environment.

To put it another way, from a personal computing perspective, the difference between local and cloud computing is the difference between saving a Google doc on Google servers and saving a Word file on your laptop or desktop.

Is My Data Safe in the Cloud?

Like a house, the cloud is only as safe as the security it has in place. Apps, websites, and other platforms that offer cloud storage are responsible for ensuring that their stake is guarded and sufficiently shielded.

Seven Tips To Keep Your Cloud Data Safe

Individuals also play a role when it comes to keeping their information safe in the cloud, and implementing a few practical tips can go a long way in avoiding a data breach.

Encrypt Data

Over four billion people are plugged into the internet, and Google processes over 40,000 searches per second! In other words, a lot of data is darting around at any given second.

Now ask yourself: what type of data would a cybercriminal target? Would they spend precious time trying to crack through a digital fortress or go for the low-hanging fruit that’s easy to access and provides the same ROI? Of course, they’re gunning for easy targets! So pull yourself out of that murky pool and only use cloud app services that encrypt data!

Run Backups

Do your cloud access providers regularly backup data? They should. Moreover, it’s best to back up personal devices on drives that don’t automatically connect to the internet.

Enable Two-Factor Authentication

Yes, two-factor authentication can be more annoying than a slow driver in the left lane, but at this point, every business should make it mandatory. Not only does it keep users safe, but it serves as an enterprise shield.

Better Password Hygiene

Password protection is an essential part of cloud safety. Individuals should never use the same one for every account, and businesses should be helping users create effective and difficult-to-crack passwords. Forcing users to change their passwords every month or two is also wise.

Do Your Homework

Would you buy a house without doing a little research about the neighborhood? Would you date a stranger without Googling them? The same logic applies to cloud apps. Before clicking “Yes, I Agree,” engage in some due diligence. Research the company and read up on red flags that rear their cautionary heads. 

Be Selective With Sensitive Information

Don’t relinquish personal or sensitive data unless necessary. Moreover, consider using services that cloak any critical financial or personal information.

Use Antivirus

Being online without an antivirus program is like crossing the Sahara without water: you’ll eventually succumb to the elements. But understand that not all antivirus options are created equal — some have the potential to do more harm than good. So be paranoid! Do a bit of review reading before installing programs on your devices.

Who is Responsible for Keeping Cloud Data Safe?

Cloud safety should be a top priority for every company, nonprofit, government, and individual. Never assume someone else is doing the job for you. Vet the apps and digital account services with which you sign up. Do they have a history of privacy problems? How long have they been around?

But overall, if you take the necessary precautions, your stuff should remain secure in the cloud.

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What is the Half Life of Data?

Half-Life of Data Mean?

The term “half-life” was originally coined by scientists studying the amount of time it takes for at least 50% of a substance to undergo an extreme change. When studying analytics and data science, the term often comes up. 

While the half-life of data isn’t as exact a measure as the half-life of substances, the implications are similar. In this case, the half-life of data is referring to the amount of time it takes for the majority of it to become irrelevant. This is an exponential curve downwards, meaning that data is at its peak value when first collected, then accelerates in loss of value over time.

In a recent study, researchers highlighted the issue that administrators often underestimate or misunderstand the half-life of their data and the implications it carries. 

What Are the Three Business Categories of Data?

Nucleus Research found in their study that businesses driving decisions with data fall into one of three categories: tactical, operational, or strategic. The half-life of data varies by the business data category.

These categories were self-identified, and no real-world business is only one of these categories. Companies in the study were asked to select a category based on four factors: their suppliers, their markets, how regulated they are, and how much they depend on intellectual property.


According to the study, the tactical category contains companies who utilize data to influence their processes in almost real-time. Because data received is extremely valuable when first received, then rapidly declines in value to the company, this category has the steepest downward curve of data half-life.

This category emphasizes how important it is for companies to have technology that allows them to act as quickly as possible on actionable data. The study found that, on average, the half-life of data in this category is a mere 30 minutes. That means data is losing a majority of its value in the first 30 minutes after collection!


The study indicates that companies using data for operational purposes generally require it to make decisions that could be anywhere from a day to a week. This is a mid-level category with a half-life curve that goes down exponentially, but far more slowly than data of companies in the “tactical” category.

Nucleus Research found that data in this category had an average half-life of 8 hours but ranged widely among companies, from one hour to 48 hours.


Companies falling into this category use data for long-term processes and plans. Strategic data’s value is the most distributed, losing value very slowly over time. The half-life of their data is a small-slope linear graph. Strategic data’s average half-life is 56 hours and widely variable.

What Are 3 Ways to Speed Up Conversion from Raw Data into Actionable Insights?

Here are three ways to divert data from silos and process it into valuable and actionable insights for your business.

Ask Good Questions – In order for raw data to be valuable, it must have a defined purpose. Meeting with all stakeholders and determining what specific question you’d like answered, then identifying data that must be collected to answer it instantly increases the value of what you’re already collecting.

Use Segmentation – If possible, differentiate among types of clients or users as much as possible. This will create more individualized and accurate insights.

Create Context – Data silos happen when large, ambiguous groups of data are collected. Ensure that everyone understands what each piece of data actually means to instantly add value to logged data.

Big Data Data Analytics

The Top 3 Most Valuable Data Visualizations & When to Use Them

Today’s dashboard software is making it easier than ever to integrate and visualize data in a way that is as inspiring as it is applicable and valuable. While doing so is simple, an exceptional dashboard still requires strategic planning and designing.

Knowing your audience will help you to determine what data you need, and knowing what story you want to present tells you which data visualization type to use. Assuming you have clean data and the best data visualization software, your next step is to choose the right charts and graphs. This article suggests – what we think are – the most valuable data visualizations any analyst could use. Based on research, personal experience, and client reviews, these suggestions are a sure fire way to present your business data with flying colors.

Sunburst Graph

This interactive, radial space-filling visualization shows the aggregated values of subtrees. This is ideal for presenting hierarchical data (Ex. store locations and their sales by product/team member/date/etc.)

This visualization shows “hierarchy” through a series of rings that are divided into multiple categories. Each ring coincides to a level in the hierarchy. The innermost circle represents the root value and the hierarchy moving outwards from it.

The rings are sliced and divided based on their hierarchical relationship to the source slice. The angle of each slice is either divided equally under its source value or can be made proportional to a value.

Different colors are typically used to highlight hierarchical groupings or certain categories.

The value in this type of visualization is in the ability to see the root cause and effect of each piece of data, based on its parent’s value. You can answer questions about changes in your data that may or may not have been caused by another piece of data. Is one value controlling the other? Will a change in a parent value affect the child value?

Co-Occurrence Matrix

With a co-occurrence matrix, a network of data values can be represented by an adjacency matrix, where each cell ij represents an edge from vertex i to vertex j.

The effectiveness of a matrix diagram is heavily dependent on the order of rows and columns: if related nodes are placed close to each other, it is easier to identify valuable clusters and bridges.

This type of diagram can be extended with reordering of rows and columns, and expanding or collapsing of clusters, to allow deeper exploration of important relationships within your data and business.

While path-following is harder in a matrix view than in a node-link diagram, matrices have other advantages. As networks get large and highly connected, node-link diagrams often devolve into giant webs of line crossings. With matrix views, line crossings are impossible. Matrix cells can also be encoded to show additional data. Colors are often used to depict clusters calculated by a “community-detection” algorithm.

Co-occurrence matrix visualizations hold value in their accessibility of relationships among every piece of your data, and how “strong” that relationship is. Does one piece of data occur more often when another, separate piece of data is also occurring more often? Or vice versa? The effects of each piece of data on one another is quite endless, and valuable, if their relationship is fact a “strong relationship”.

matrix data visualization

Choropleth Map

The purpose of a choropleth map is to display geographical areas or regions that are colored, patterned, or shaded based on a specific data variable. This gives the user a way to visualize data values over a geographical area, showing variation or patterns across the available location. Choropleth maps give us the ability to represent a large amount of data over any amount of space in a concise and visually engaging manner.

The data value uses “color progression” to represent itself in each region of the map. This can be blending from one color to another, transparent to opaque, hue progression, light to dark, or an entire spectrum of colors.

There are 3 basic criteria necessary to use a choropleth map:

  1. The data is spatially related (i.e. countries, states, counties), or “enumeration units”
  2. Data is not raw; it has been processed to reveal rates/statistics/ratios
  3. The data could be collected and used anywhere in space

These criteria quickly reveals the fact that to effectively use a choropleth map, the purpose must be statistically related, and be able to freely cover any area in space.

For any business that produces data over a geographical area – sales, political, population, etc. – a choropleth map is your best visualization option to display growth/success/comparisons of that data over the respective area in an instant. Most choropleth maps are also interactive, giving you the ability to drill down into each geographical area’s data results by simply moving your mouse over that area.

The value a choropleth map provides is simple: instant comparable geographical data representation. Are your east coast sales doing better than your west coast sales? Is your political campaign more successful in one county than in another? The answers provided about your geographical data are endless.

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Big Data

5 Data Analysis Methods to Up Your Data Game

In the wake of the Big Data age, everyone seems to be talking about data. Data is at the center when it comes to industry news, board meetings, and almost every strategy or new project moving forward. Even job descriptions for non-traditionally data-focused roles are looking for candidates with the ‘data-driven mindset.’ As a result, the way we do business is rapidly evolving and it’s clear that data is here to stay.

Despite all of the talk and enthusiasm surrounding data, though, what are organizations doing with this newfound data-driven focus? How do you go about actually transforming data into actionable insights? How do you determine the right approach when analyzing your data?

There are a number of techniques and methods to choose from when analyzing your data. In this post, we’ll explore a few of the most common and effective data analysis methodologies to help you maximize your approach when working with data.

1. Regression Analysis

Regression analysis is the statistical process of estimating relationships between one dependent variable and one or more independent variables. The focus here is on determining which variables could have a possible impact on the chosen dependent variable. The most common end goal of regression analysis is to identify patterns and predict future trends.

It’s important to note that there are multiple forms of regression analysis, each varying based on the type of data being analyzed and the nature of the variables involved. Overall, regression models remain an effective way to highlight casual relationships and make inferences about those relationships.

2. Monte Carlo Simulation

The Monte Carlo simulation, also known as the Monte Carlo method, is a mathematical technique used to evaluate the probability of certain outcomes and events occurring. Through random sampling and specified parameters, the simulation can be run repeatedly to produce a thorough range of probable results. The more times the simulation is run, the more accurate the range of possibilities will likely be. This methodology is particularly useful when assessing potential risks and to aid the decision-making process.

3. Data Mining

Data mining is an interdisciplinary field that combines a number of machine learning and statistical processes. There are many different techniques that fall under the data mining umbrella, from data preparation to clustering and classification. It is also all about identifying patterns amongst large sets of data from multiple sources to generate new insights. The end goal, though, is to identify areas of improvement, opportunity, and optimize costs. 

To learn the major elements and stages of data mining, also read: What is Data Mining?

4. Sentiment Analysis

Sentiment analysis, also referred to as opinion mining or emotional AI, focuses on the analysis of qualitative data. Sentiment analysis is the combination of text analysis, natural language processing, and other computational techniques to determine the attitude or opinions behind data. This method helps analysts easily determine whether the response or viewpoint on a topic is positive, negative, or neutral. Companies commonly use this form of analysis to determine customer satisfaction levels and access their brand reputation. Data collection can be achieved through informal channels such as product reviews or mentions on social media. 

For a more in-depth look into sentiment analysis, also read: Modeling Intent & Anticipating Outcomes with Sentiment Analysis

5. Hypothesis Testing

Hypothesis testing is a statistical approach that allows analysts to test assumptions against the parameters of their chosen population. Through testing sample data one can determine the probability that their hypothesis is correct. This method is helpful in making predictions on the effects of decisions before they’ve been made. For example, say you have a theory that increasing your advertising spend will lead to higher sales. Hypothesis testing would allow you to test the validity of your claim, based on your previous sales data or data collected through a generation process, to make a more informed decision. Choices that seem obvious or guaranteed to succeed might not have the desired effect you’d think. This makes the importance of testing and validating your claims all the more important to avoid costly mistakes.

Big Data Data Preparation

Discrete Data vs. Continuous Data: What’s the Difference?

We create data every day, oftentimes without even realizing it. To put a number on it, it’s estimated that each day we create 2.5 quintillion bytes of data worldwide. Tasks as simple as sending a text message, submitting a job application, or streaming your favorite TV show are all included in this daily total. However, not all of this data is created equal.

Similar to the many unique ways there are to create data, there is also a corresponding array of various data types. Data types are important in determining how the data is ultimately measured and used to make assumptions.

Let’s get down to the fundamentals of numeric data types as we explore discrete data, continuous data, and their importance when it comes to Big Data and analytics.

Numeric Data Types

Numerical data types, or quantitative data, is what people typically think of when they hear the word “data.” Numerical data types express information in the form of numbers and assign numerical meaning to data. There are two primary types of numerical data: discrete and continuous data.

What is Discrete Data?

Discrete data also referred to as discrete values, is data that only takes certain values. Commonly in the form of whole numbers or integers, this is data that can be counted and has a finite number of values. These values must be able to fall within certain classifications and are unable to be broken down into smaller parts.

Some examples of discrete data would include:

  • The number of employees in your department
  • The number of new customers you signed on last quarter
  • The number of products currently held in inventory

All of these examples detail a distinct and separate value that can be counted and assigned a fixed numerical value. 

What is Continuous Data?

Continuous data refers to data that can be measured. This data has values that are not fixed and have an infinite number of possible values. These measurements can also be broken down into smaller individual parts.

Some examples of continuous data would include:

  • The height or weight of a person
  • The daily temperature in your city
  • The amount of time needed to complete a task or project

These examples portray data that can be placed on a continuum. The values can be continually measured at any point in time or placed within a range of values. The distinguishing factor being that the values are measured over time rather than fixed.

Continuous data is commonly displayed in visualizations such as histograms due to the element of variable change over time.

Discrete Data vs. Continuous Data

Discrete and continuous data are commonly confused with one another due to their similarities as numerical data types. The primary difference, though, between discrete and continuous data is that discrete data is a finite value that can be counted whereas continuous data has an infinite number of possible values that can be measured.

If you’re questioning whether or not you’re working with discrete or continuous data, try asking yourself questions such as:

  • Can these values be counted?
  • Can these values be measured?
  • Can these values be broken down into smaller parts and still make sense?

The Importance of Numerical Data Types

Discrete and continuous data both play a vital role in data exploration and analysis. Though it is easy to review definitions and straightforward examples, data is often filled with a mixture of data types. Making the need to be able to identify data types all the more important.

Additionally, many exploratory methods and analytical approaches only work with specific data types. For this reason, being able to determine the nature of your data will make handling your data more manageable and effective when it comes to yielding timely insights.

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