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Author: Scottie Todd

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Big Data Business Intelligence Data Analytics

Top 5 Growing Data and Analytics Trends for 2021

Bar Graph Statistics Analysis Business Concept
Bar Graph Statistics Analysis Business Concept

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.

Conclusion

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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Business Intelligence Data Analytics Data Visualization

What is a KPI Dashboard?

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Businesswoman using a tablet to analysis graph company finance strategy statistics success concept and planning for future in office room.

What is a KPI Dashboard?

Every day your business collects hundreds or even thousands of data points, and it can be overwhelming to wade through all of this information. Key Performance Indicators (KPI) are the different metrics used to access if you’re reaching your business objectives and goals. 

There are numerous KPIs that businesses can use, but the most common are Quantitative, Qualitative, Lagging, and Leading KPIs. 

Quantitative KPIs deal with numerical data such as currency, percentages, and statistics. Qualitative KPIs, on the other hand, takes into account the interactions of your customers. It factors their opinions, experiences, and even feelings towards your business or product. 

Lagging KPIs takes a look at historical data and uses it to predict future outcomes. Most Leading KPIs, though, look at performance for future forecasting. Typically these will be used together to help increase your overall visibility. 

Looking at all of this data separately can be both time-consuming and inefficient in the use of resources. A KPI dashboard can look at all of this data and provide you with key information at a glance. That allows you to monitor goal performance, find ways of improving workflows, and make sure that you’re making the best use of your resources. 

Who Can Benefit From a KPI Dashboard?

Businesses that are already gathering any form of operational or transactional data can benefit from a KPI dashboard. Organizations that need to make adjustments to their workflow or data collection processes would also benefit from a KPI dashboard. They can see information at a glance to know if the business is over or underperforming in regards to trends, quarterly goals, and business strategy. 

Companies that have numerous departments or organizational levels can take advantage of a KPI dashboard. Each business function may have its own individual goals and the dashboard can help paint a clearer picture of how that fits into the organization as a whole. That way you can manage your target strategies and identify which areas of your operations need to be streamlined. 

What Are Some of the Benefits of a KPI Dashboard?

A KPI dashboard can provide you with new insights into how your business is performing. Whether you want a high-level view or need to drill down for more detailed information, you can customize reporting to meet the needs of your business or department. Additionally, the KPI dashboard can also be customized to meet the needs of individual users. That way the data can be tailored to what’s relevant for each person’s role and daily responsibilities. 

Some examples of KPIs that you can put into your dashboard can include:

  • Revenue per customer
  • Project time
  • Churn rates
  • Net profit
  • Revenue growth

Depending on the size of your business, you could be using hundreds of KPIs. Regardless, dashboards will work to filter and highlight the key information. 

Another benefit of a KPI dashboard is that you can scale the data. You can get down to individual performance metrics, departments, or teams so that you can easily access desired information and find ways of improving performance. 

Using a KPI dashboard can also help businesses make decisions about their investments. You can get real-time updates to monitor the ROI and use historical data to forecast profitable future investments.

Why Should I Use a KPI Dashboard for My Business? 

When building a KPI dashboard, you should consider metrics that are relevant to the goals of your business. Examine the various stakeholders in the organization and access who will need to access the data, then choose the KPIs that align with their goals and strategies. 

When selecting individual KPIs, you should focus on actionable metrics. Be specific with your goals and focus on the ones that are of the highest priority. If the business strategy changes, you can always go back and adjust the KPIs to realign with your new approach. At the end of the day, the KPI dashboard that you use should help improve your business. 

As your goals and strategies change, the KPI dashboard should be robust enough to adapt as well. A KPI dashboard that works well for one industry may not work for yours, so it’s important to do your research and find the right components for your business.

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Big Data Data Preparation Data Quality

The Costly Compound Effect of Bad Data in Your Warehouse

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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Big Data Business Intelligence Data Analytics

The Real Competitive Advantage of Real-Time Analytics

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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Big Data Data Analytics

7 Effective Tips to Secure Your Cloud Data

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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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Artificial Intelligence Data Analytics Data Preparation

Growth Hacking Your Business Processes with Artificial Intelligence

Growth Hacking Your Business Processes With Artificial Intelligence

Along with data and analytics, the focus on continuous improvement remains a constant in the business world. Recent market disruptions have only emphasized the need for businesses to optimize their processes and core functions moving forward. Artificial intelligence is one tool companies are turning to when achieving greater efficiency within their operations. Let’s explore how AI is transforming the way we do business, from data cleaning to the customer experience.

Using AI to Cleanup Dirty Data

Let’s get straight to the point. Dirty data costs businesses money, regardless of if they heavily rely on or prioritize data in their operations. The average cost of dirty data sings to the tune of around 15% to 25% of revenue each year. While this percentage doesn’t appear to be an overwhelming amount, consider the overarching estimate from IBM that bad data costs the U.S. $3.1 trillion each year. This high cost is mainly due to the complexities associated with cleaning and maintaining an organization’s data quality. 

There’s no question that data cleaning is a lengthy and time-consuming process. As a result of this, less time is able to be devoted to high-level goals. Decision-makers have long wait times when it comes to converting raw data into actionable insights. AI, though, is able to automate this process so businesses can focus their efforts elsewhere. AI learns from each data set and can detect columns in need of cleaning, all while simultaneously updating the data model. The productivity of your data science team is improved, saving hundreds of hours that would have been spent on cleaning tasks.

Analyzing Business Data for Forecasting and Prediction

The use of business data to identify patterns and make predictions is well established. Using AI-powered tools and solutions, any business user can generate insights quickly without advanced programming or data science skills. The ease of use makes this process faster, more accessible, and efficient across business units. This reduces miscommunications between the analytics team and eliminates wait times on reports, query requests, and dashboard delivery.

Additionally, exploding data volumes have made effective use of an organization’s data difficult to manage. Artificial intelligence helps to quickly analyze these large volumes in record time, allowing for faster insights along with higher quality forecasting. Actionable business data is becoming accelerated with the use of AI, helping business leaders make decisions with greater accuracy.

Improving Sales and Customer Success

AI-powered analytics is helping companies gain insights into their prospects as well as current customers. For instance, companies can use AI in conjunction with their CRM data to predict which customers are most likely to cancel their subscriptions or which new sales accounts are more likely to close. These predictions can be flagged to alert the customer success team or sales staff, highlighting where they should be maximizing their time. This acceleration can also result in a more efficient and effective customer lifecycle.

On the customer experience side of things, process improvement can also be established through automatic support lines and AI-powered chatbots. AI systems can monitor customer support calls and detect detailed elements as minuscule as tone to continually keep an eye on quality. Chatbots also offer additional availability for immediate support. Problems are identified and resolved faster to increase revenue along with customer retention.

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What is NoSQL? Non-Relational Databases Explained

What Is NoSQL Non-Relational Databases Explained

Non-tabular NoSQL databases are built on flexible schemas, with nested data structures, to accommodate modern applications and high user loads. Increasingly, they’re the optimal choice when ease of development, scaled performance, speed, and functionality are central operating concerns.

What is NoSQL?

NoSQL stands for “not only SQL” to signify that it accommodates various data access and management models. “Nonrelational database” is also interchangeable with “NoSQL database.” Programmers developed the language in the mid-2000s alongside the widespread adoption of new information trends. 

Some people insist that NoSQL databases are sub-par when it comes to storing relational data. However, this is an unfair argument. Nonrelational databases can handle relationship data; it’s just done differently. Plus, other folks may argue that relationship modeling is more manageable in the NoSQL environment because data isn’t parsed between multiple tables.

What? Can You Please Explain in Non-Tech Talk?

Does this all sound like Greek to you? If so, think of SQL and NoSQL as vinyl records and digital music streaming, respectively. To play a song on a vinyl album, you must physically move the needle on the turntable to access the “track” you want because the record was pressed ahead of time, and that’s where the desired “files” reside. Thanks to remote access memory, however, when streaming digital music, you can just press or click to play a song — even though all the parts making up the file may be scattered.

It’s not a perfect analogy, but the technological gist falls into the same category.

A Brief History of NoSQL

Back in the second half of the 20th century, when digital computing was still in its infancy, relatively speaking, data storage was one of the most expensive aspects of maintaining data sets. But by the late 2000s, on the back of Moore’s Law, those costs had plummeted, and developers’ fees were instead topping cost lists.

Back then, social media and digital networking were skyrocketing in popularity, companies were inundated with mounds of raw data, and programmers were adopting the Agile Manifesto, which stressed the importance of responding to change instead of remaining slaves to procedural protocol. As such, new tools were developed to accommodate the influx of information and the need to be highly flexible.

The practical and philosophical shift served as an inflection point and changed the course of computing history. Developer optimization was prioritized, flexibility took priority, and the confluence of events led to the creation of NoSQL databases.

NoSQL v. SQL

Before NoSQL, there was SQL — the querying language used for relational database environments where tables are constrained and connected by foreign and primary keys.

On the other hand, NoSQL allows for greater flexibility and can operate outside the confines of a relational database on newer data models. Plus, since NoSQL-friendly databases are highly partitionable, they’re much easier to scale than SQL ones. 

To be clear, SQL is still used today. In fact, according to reports, as of May 2021, approximately 73 percent of databases run on the SQL model — but NoSQL is rapidly expanding its market share.

Why Do Developers Use NoSQL?

Developers typically use NoSQL for applications that process large amounts of data and require a low latency speed. It’s achieved by easing up on consistency restrictions and allowing for different data types to commingle.

Types of NoSQL Databases

There are five main types of NoSQL databases.

  • Key-Value: Key-value databases can be broken up into many sections and are great for horizontal scaling.
  • Document: Document databases are great when working with objects and JSON-like documents. They’re frequently used for catalogs, user profiles, and content management systems.
  • Graph: Graph databases are best for highly connected networks, including fraud detection services, social media platforms, recommendation engines, and knowledge graphs.
  • In-Memory: In-memory databases are ideal for apps requiring real-time analytics and microsecond responses. Examples include ad-tech and gaming apps that feature leaderboards and session stores.
  • Search: Output logs are an important part of many apps, systems, and programs. Search-based NoSQL databases streamline the process.

In a nutshell, NoSQL is a database revolution that’s helping drive tech innovation.

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Big Data Data Analytics

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

abstract

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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Business Intelligence Data Analytics

The Comprehensive Guide to Healthcare Analytics

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What is Healthcare Analytics?

The healthcare analytics field involves collecting and analyzing data from health services in order to make improved medical decisions in the future. The goal of the field is to support overarching health domains, ranging from prescriptions to microcosmic areas such as rare diseases. Better care can be given to patients in less time than ever before due to the introduction of BI and healthcare analytics tools. 

Depending on the field of medical practice in which healthcare analytics software is used, the benefits vary. For example, a small local practitioner may see the biggest benefit in having access to public health information derived from large hospital groups. Through this, the practitioner may be able to gain insights they otherwise would not have access to. On the other hand, the largest benefit that a large hospital may see in healthcare analytics could be streamlining patient charts and records. This can significantly lower the chances of losing records and ensures flexible access to needed information.

How Does the Healthcare Industry Use Healthcare Analytics?

The healthcare industry uses healthcare analytics to support services on all fronts. From ensuring positive patient experiences to lowering readmission rates to payer and insurance services, healthcare analytics has a wide array of purposes.

The high-risk patient population is one demographic assisted by healthcare analytics. This type of software digitizes healthcare records and leverage Artificial Intelligence (AI) to easily flag and identify high-risk patients. Physicians can utilize this data to divert patients from potential emergency room visits down the line. Extremely intricate risks, such as a rare polydrug reaction that can only occur with certain uncommon diseases, can be instantly highlighted and then mitigated by the prescribing physician.

Human error is also significantly cut down by healthcare analytics. Anomalies in prescription dosages can be found before a patient is prescribed the wrong amount. Both doctors and insurance companies can automate lengthy claims processes, allowing doctors to spend more time one-on-one with patients and less time haggling with insurers. In the most significant cases, even accidental death with lasting medical, fiscal, and personal problems attached can be prevented; this is particularly beneficial for larger offices with more doctors and patients since the onus is no longer solely on the doctor to have a clear and comprehensive view of the patient.

How Healthcare Analytics is Transforming Healthcare

Healthcare is rapidly evolving, primarily due to innovations in healthcare analytics software. Business Intelligence (BI) is one such innovation that’s been a game-changer. The costs of operation, workflow, and automated decision-making software involved are all evolving over time. Indirectly, healthcare facilities and practitioners benefit from data aggregated and analyzed from other facilities to help each other identify public health issues like COVID-19, as we’ve seen over the past couple of years.

Another more recent evolution in healthcare analytics is Population Health Management (PHM). This is a more modern approach to health; while traditional healthcare is reactive to situations that emerge, PHM focuses on preventing possible issues that could occur in the future. This is far more efficient in terms of time and money, but it requires predictive modeling in order for it to work in the public sector.

To perform PHM using healthcare analytics software, there must be a first, large initial data set. Using this, specific diagnoses can be analyzed and patterns can be found. In other words, AI can essentially perform medical research on very specific populations to inform doctors on public health problems and how they may work to help lower incidences in their local communities.

Wrapping Up Healthcare Analytics

It’s clear that healthcare analytics software is used extensively across the strata of medicine. Patients see instant value in this because it makes everything from new patient signup to paying copays much easier and more streamlined. Practitioners also see an instant return on investment from healthcare analytics by lessening manual research time and administrative headaches. Even insurance companies benefit, as do their customers, by being able to process items like prior authorizations and the like at a much faster rate than ever before.

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7 Ways to Optimize Your SQL Queries for Production Databases

According to Google, 53 percent of mobile users will abandon a website if it doesn’t load within three seconds — and the bounce rates for PCs and tablets aren’t that much more. So what do these stats mean for coders? Ultimately, they’re a stark reminder that crafting optimized SQL queries for production database environments should be a priority.

What Are SQL Queries and Production Databases?

New programmers may be wondering: What are SQL queries and production databases? At first, the terms may sound intimidating. But the reality is simple: SQL queries are simply the code you write to extract desired records from a database, and a production database just means “live data.”

In other words, a dynamic website that’s live and accessible is likely working off a production database.

Why Should SQL Queries Be Optimized?

Which would you rather read: a loquacious tome freighted with filler words and pretentious tangents or a tl;dr summary that zeros in on the topic at hand? Moreover, which would take longer to digest? 

The same keep-it-simple logic applies to writing in SQL: the best queries are short, sweet, and get the job done as quickly as possible — because cumbersome ones drain resources and slow loading times. Plus, in the worst-case scenarios, sloppy queries can result in error messages, which are UX kryptonite.

Seven Ways to Optimize SQL Queries for Production Databases

#1: Ask the Right Questions Ahead of Time

Journalists have long understood the importance of who, what, when, where, and how. Effective coders also use the five questions as a framework. After all, every business has a purpose, and, like a finely tuned car, every mechanism should support the company’s ultimate goal — even the SQL queries that power its websites, databases, and reporting systems.

#2: Only Request Needed Data

One significant difference between novice programmers and experienced ones is that individuals in the latter category write elegant queries that only return the exact data needed. They use WHERE instead of HAVING to define filters and avoid deploying SELECT DISTINCT commands unless absolutely necessary.

#3: Limit Sources and Use the Smallest Data Types

If you don’t need a full report of matching records, or you know the approximate number of records that a query should return, use a LIMIT statement. Also, make sure to use the smallest data types; it speeds things up.

#4: Be Minimalist, Mind Indexes, and Schedule Wisely

Choose the simplest and most elegant ways to call up needed data. To state it differently, don’t over-engineer. Moreover, make use of table indexes. Doing so speeds up the query process. Plus, if your network includes update queries or calls that must be run daily, schedule them for off-hours!

#5: Consider Table Sizes

Joining tables is an SQL query staple. When doing it, make sure to note the size of each table and always link in ascending order. For example, if one table has 10 records and the other has 100, put the former first. Doing so will return the desired results and cut down on query processing time.

#6: Only Use Wildcards at the End

Wildcards can be a godsend, but they can also make SQL queries unruly. By placing them at the beginning and end of a variable, you’re inefficiently forcing the broadest possible search. Instead, get specific. And if you must use a wildcard, make sure it’s at the end of a statement.

#7: Test to Polish

Before you put a project to rest, test! Try different combinations; whittle away at your queries until they’re elegant code blocks that make the least number of database calls. Think of testing as the editing stage, and revise until the work is polished.

Who Should Tweak SQL Queries?

People with little or no coding experience may be able to DIY a small CSS change or add an XHTML element without catastrophe. But SQL queries are a very different story, one errant move can wreak mayhem across your operations.

Optimizing SQL queries is essential in today’s digital landscape, and failing to do so can lead to decreased views and profits. So make sure to optimize the code before going live. And if you don’t have the experience, enlist the help of someone who does. It’s worth the investment.

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