Big Data Business Intelligence Data Analytics

Why You Need to Modernize Your Data Real Estate

How Does Your Company’s Data Real Estate Measure Up?

Are you still letting your gut guide business and promotional plans? In today’s market, where nearly 60 percent of companies leverage “big data” and growth statistics indicate a 5,000 percent industry increase over the past 10 years, it’s a dangerous choice — especially since that number continues to grow. Before long, data-rooted marketing and procedural initiatives will become as commonplace as the Internet.

This industry push toward informational analytics begs the question: How is your company’s digital data game? Are you keeping up with the times or lagging woefully behind? 

Why Is Data So Important These Days?

Data is like a crystal ball. It provides insight into market trends, customer behavior, and back-office logistics. Companies that invest in informational architecture tend to save money and increase efficiency, giving them a competitive edge. 

What Is Data “Real Estate?”

Data “real estate” refers to the software, hardware, and reporting mechanisms a business uses to collect, sort, and analyze raw data. The phrase can also encompass your informational pipeline and procurement methods. 

How To Modernize Your Data Real Estate?

Decades ago, when businesses first started leveraging data, most IT analytics tools were static and limited. Microsoft Excel and Access were the big players back then. In short order, relational databases popped onto the scene, but early options required lots of human data entry, and they lacked dynamism.

If you’re still paddling in that data puddle, it’s time to modernize. Today’s options are light-years ahead, and they’ll likely improve your bottom line in the long run. 

Embrace Automation and Merge Your Lakes

Automation advancements have seismically changed the data pipeline landscape. Today’s programs can handle many routine parsing, cleaning, and sorting tasks. What once took hours now takes minutes. Additionally, auto-correction and other machine-learning innovations have significantly improved data accuracy. 

Streamline Your Data Flow: Moving from ETL vs. CDC

The next step in modernizing your data real estate is moving from an ETL environment to a CDC one. ETL stands for “extract, transform, load,” while CDC represents “change data capture.” We could write a dissertation on the technical differences between the two methodologies, but for the purposes of this conversation, suffice it to say that the latter provides a constant stream of fresh data while the former is more of a traditionally manual process.

Now here’s where things get a little bit confusing. CDC uses ELT, which stands for “extract, load, transform” — the next generation of ETL, which allows for better speed and fluidity.

The Future Is Now, And It’s Data-Driven

In days of old, when Mad Men ruled Madison Avenue, business acumen was more of a talent than a science. And while it still takes competency and knowledge to run a successful company, data analysis removes a lot of the guesswork. 

The margin of error is becoming increasingly narrow, and leveraging big data will help ensure that you keep a competitive edge.

BI Best Practices Business Intelligence

Self-Service Analytics: Turning Everyday Insight Into Actionable Intelligence

Business intelligence and analytics have become essential parts of the decision-making process in many organizations. One of the challenges of maximizing these resources, though, comes with making sure everyone has access to the analysis and insights they need right when they need them. The solution you may want to consider is self-service BI.

What is Self-Service BI?

The idea behind self-service BI is simple. Users should be able to access reports and analysis without depending on:

  • An approval process
  • A third party
  • Any specific person in the organization

In other words, everyone should be able to ask the person to their left to pull something up. If the boss needs to hear what the details of a report are, their team should be able to access key information without contacting a help desk or a third-party vendor. When they need help, anyone from the top down should be able to instantly address the issue by pointing them to the proper dashboards and tools.

Defining Your Requirements

Before getting too deep into the complexities of self-service BI, it’s important to establish what your requirements are. First, you’ll need to have the resources required to provide self-service to your end-users. If you’re going to have 10,000 people simultaneously accessing dashboards from locations across the globe, that’s a huge difference compared to a company that has 5 people in the same office on a single system.

Scalability is an extension of that issue. If your company has long-term growth plans, you don’t want to have to rebuild your entire analytics infrastructure three years from now. It’s important to build your self-service BI system with the necessary resources to match long-term developments.

Secondly, you’ll want to look at costs. Many providers of BI systems employ license structures, and it’s common for these to be sold in bulk. For example, you might be able to get a discount by purchasing a 500-user license. It’s important that the licensing structure and costs must match your company’s financial situation.

Finally, you need to have a self-service BI setup that’s compatible with your devices. If your team works heavily in an iOS environment on their phones, for example, you may end up using a different ecosystem than folks who are primarily desktop Windows users.

Developing Skills

A handbook has to be put in place that outlines the basic skills every end-user must have. From a data standpoint, users should understand things like:

  • Data warehousing
  • Data lakes
  • Databases

They also should have an understanding of the BI tools your operation utilizes. If you’re using a specific system in one department, you need to have team members who can get new users up to speed company-wide. You’ll also likely need to have team members who are comfortable with Microsoft Excel or Google Sheets in order to deal with the basics of cleaning and analyzing data.

Your users need to be numerate enough to understand broad analytics concepts, too. They should understand the implications of basic stats, such as why small sample sizes may hobble their ability to apply insights to larger datasets.

Understand How Users Will Behave

Having the best tools and people in the world will mean nothing if your team members are always struggling to work the way they need to. This means understanding how they’ll use the system.

Frequently, user behaviors will break up into distinct clusters that have their unique quirks. Someone putting together ad hoc queries, for example, is going to encounter a different set of problems than another user who has macros set up to generate standard reports every week. Some users will be highly investigative while others are largely pulling predefined information from the system to answer questions as they arise.

Within that context, it’s also important to focus on critical metrics. Team members shouldn’t be wandering through a sea of data without a sense of what the company wants from them.

By developing an enterprise-wide focus on self-service BI, you can help your company streamline its processes. When the inevitable time comes that someone needs a quick answer in a meeting or to make a decision, you can relax knowing that your users will have access to the tools, data, and analysis required to do the job quickly and efficiently.

Business Intelligence Data Visualization

7 Golden Rules for Dashboard Design

There are some important rules to follow when making dashboards and to ensure your dashboard hits the mark it needs to and gets people using it. Now let’s dive deeper into what makes a good dashboard.

1. Make sure you are designing the right dashboard for who is going to be using it

If you are going to spend your time and energy making a good dashboard you need to know who the dashboard has to be “good” for. Executives will want to see something different from a salesperson and so on for a data analyst, you need to know about the audience of the dashboard. The most effective dashboards are those which target a single user or group of users and then provide the right data and visualizations accordingly. A lot of people overlook this step, which creates havoc later on.

2. Display the dashboard data in a logical manner

Ensure that your data is displayed in logical groups, and in a proper manner. The top left-hand corner of your dashboard is where the eye will immediately go to and as such, it is the most important part of the dashboard. When we say to display the data in logical groups that is to say; if you are making an executive overview that compares two marketing strategies, the data for each strategy should be grouped together so that all of the visualizations for strategy A go on the left and all of the visualizations for strategy B go on the right.

3. Vary your visuals

When it comes to dashboards, variety is always better. Don’t use only one chart type, no matter how much you love stacked bars. Mix it up with straight lines and curves. Vary your report elements between low-level tables that show granular data and higher-level pie charts, gauges, and value widgets.

Here is an example of a good variety of visualizations for a monthly recap. It immediately shows the important totals as well as a comparison to last month and where our customers are coming from.

4. Locate the most important visuals “above the fold”

Above the fold is an old newspaper term, and it means the top part of the front page, which is still visible when the paper is folded. For dashboards, this means the screen real estate at the top is visible when the dashboard is first loaded by the user. Put your most important and/or summary level visuals at the top. Tools like Inzata ( let you view heatmaps of how your users scroll and navigate within their dashboards. Use these to identify their most important and most-viewed elements and move them near the top.

5. Don’t forget your headline

You’re probably tired of all the newspaper references, but hey, there’s a reason why nearly every print newspaper on Earth is organized in more or less the same way: it’s effective.

What do headlines do? They communicate the main message to the reader about what happened or is happening. They’re in big, bold print so they’re hard to miss. We recommend using a nice row at the top consisting of 3 or 4 easy-to-spot widgets that are easy to read, like gauges or single-value widgets. This “headline” row quickly delivers key information to the viewer and sets the stage for further exploration as they continue reading.

6. Always refresh your data

Data that is being displayed on your dashboard only maintains importance if it is up to date. If you are trying to decide what to do tomorrow based on data from three months ago, you are going to arrive at the wrong conclusions.

7. Keep your dashboards focused!

Make sure that the most important data gets highlighted in the dashboard. You should also keep your dashboard small so that it is able to focus on the thing which is most important. Long dashboards aren’t always the best. Many small dashboards, which are focused on a single topic or goal, make a better dashboard than long and heavy ones.

Your dashboard should be focused on answering a single question and should almost never have more than five or six visualizations. It may be tempting to make one huge dashboard where someone could find all the answers, but that is not how people’s brains typically work. It will result in users feeling lost and confused because they can’t find what they need quickly. If it takes someone more than 5 seconds to find what they need on your dashboard, consider redesigning it.

Big Data Business Intelligence

Making Sense of IoT Sensors, MQTT, and Streaming Data

With the use of IoT sensors on the rise, one of the great challenges companies face is finding a protocol that’s both compact and robust enough to meet a variety of requirements. IoT devices oftentimes need to be able to communicate on a machine-to-machine (M2M) basis, and they also need to transmit information to servers, analytics platforms, and dashboards. Similarly, they may need to provide streaming data to all of these platforms.

One solution many organizations have settled on is Message Queuing Telemetry Transport (MQTT). Created by IBM in 1999, MQTT is a very mature protocol compared to other available options. Let’s take a look at why MQTT is a strong candidate for widespread adoptions over the coming decade and some of its best use cases.

What’s in a Protocol?

It may be helpful to think generically about what makes a transport protocol ideal for deployment in IoT sensors and devices. Qualities worth including in such a protocol include:

  • Very low power consumption
  • A light code footprint that can be adapted to many small devices
  • Minimal bandwidth usage
  • Low latency
  • Compatibility with a wide range of public clouds
  • A simple publication and subscription model

MQTT ticks all the boxes, providing support to a variety of major platforms. It was originally intended to allow oil pipeline systems to communicate with satellites. Deployed in sometimes difficult conditions, MQTT is built to keep power and bandwidth requirements minuscule. It also offers robust library support for popular programming languages like Python.

How MQTT Works

A publication and subscription model is the core of MQTT. Individual devices are set up as clients, but the central systems they communicate with are considered brokers rather than servers. If a client wants to send information out, it will publish the data to a topic. The broker then sends the information to all other clients that have subscribed to receive publications on the topic.

This is ideal for use with sensors because they don’t need to know anything about what’s occurring upstream. Also, all clients on the network have the capacity to be publishers and subscribers. They simply check in with the broker to find out what’s new.

Using MQTT with Streaming Data

IoT devices oftentimes use fire-and-forget solutions to minimize bandwidth and power consumption. For example, a Raspberry Pi might be set up as a monitoring station in a cornfield to provide data regarding things like air and soil temperatures, humidity, hydration, and pH levels. In the simplest form, the farmer’s data dashboard is just one more client in the network. Each of the sensors publishes data, and the dashboard, acting as just another client, subscribes to the topics from all of the sensors.

The beauty of this system is fairly self-evident. No one has to deal with massive server-client infrastructure. The farmer can easily have clients set up on a cellphone, tablet, in-vehicle display and laptop. Information is available everywhere and at all times, and this is all accomplished with little power consumption, a light central broker, and minimal bandwidth consumption. This represents a very lean approach to streaming data.

Two Use Cases

Logistics firms frequently use MQTT to track fleets and shipments. A system using MQTT can connect sensors in planes, trains, trucks and cars with a company’s existing backend for analytics and storage. Likewise, computers and mobile devices can bypass the cumbersome backend by talking directly to the MQTT system, providing nearly real-time information.

Despite its rather heavy-duty industrial pedigree, MQTT has found its way into a surprising variety of applications, too. For example, MQTT is a core component of Facebook Messenger. The company elected to use MQTT because its low power consumption helped it preserve battery life on mobile devices.


Having a lightweight protocol is essential to maximizing the efficiency and effectiveness of IoT devices and sensors. MQTT is one of the more appealing options for companies that need to prioritize speed and simplicity. If you’re preparing to deploy or upgrade a network of IoT systems, MQTT will be one of the options on your shortlist when it comes to choosing a protocol.

Data Science Careers

Citizen Data Scientist vs. Data Scientist: What’s the Difference?

Over the past decade, businesses and organizations have come to rely on the competitive edge afforded by predictive analytics, business modeling, and behavioral marketing. And these days, enlisting both data scientists and citizen data scientists to optimize information systems is an effective way to save money and squeeze the most from data sets.

What is a Citizen Data Scientist?

Citizen data scientist is a relatively new job description. Also known as CDSs, they are low- to mid-level “software power users” with the skills to handle rote analysis tasks. Typically, citizen data scientists use WYSIWYG interfaces, drag-and-drop tools, in addition to pre-built models and data pipelines.

Most citizen data scientists aren’t advanced programmers. However, augmented analytics and artificial intelligence innovations have simplified routine data prep procedures, making it possible for people who don’t have quantitative science backgrounds to perform a scope of tasks.

Except in the rarest of circumstances, citizen data scientists don’t deal with statistics or high-level analytics.

At present, most companies underutilize CDSs. Instead, they still hire experts, who command large salaries or consulting fees, to perform redundant tasks that have been made easier by machine learning.

What is a Data Scientist?

Data scientists — also known as expert data scientists — are highly educated engineers. Nearly all are proficient in statistical programming languages, like Python and R. The overwhelming majority earned either master’s degrees or PhDs in math, computer science, engineering, or other quantitative fields.

In today’s market — where data reigns supreme — computational scientists are invaluable. They’re the brains behind complex algorithms that power behavioral analytics and are often enlisted to solve multidimensional business challenges using advanced data modeling. Expert data scientists work with structured and unstructured objects, they also often devise automated protocols to collect and clean raw data.

Why Should Companies Use Both Expert and Citizen Data Scientists?

Since CDSs cost significantly less than qualified scientists, having both citizen and expert data engineers in the mix saves money while allowing your business to maintain a valuable data pipeline. Plus, data engineers are in short supply, so augmenting their support staff with competent CDSs is often a great solution.

Some companies outsource all their data analytics needs to a dedicated third party. Others recruit citizen data scientists from within their ranks or hire new employees to fill CDS positions.

How to Best Leverage Citizen Data Scientists and Expert Data Scientists

Ensuring your data team hums along like a finely tuned motor requires implementing the five pillars of productive data work.

  1. Document an Ecosystem for CDSs: Documenting systems and protocols makes life much easier for citizen data scientists. In addition to outlining personnel hierarchies, authorized tools, and step-by-step data process rundowns, the document should also provide a breakdown of the company’s goals and how CDS work fits into the puzzle.
  2. Augment Tools: Instead of reinventing the wheel, provide extensions to existing programs commonly used by citizen data scientists. The best augmentations complement CDS work and support data storytelling, preparation, and querying.
  3. Delegate: Pipelines that use both expert and citizen data scientists work best when job responsibilities are clearly delineated. Tasks that require repetitive decision-making are great for CDSs, and the experts should be saved for complex tasks.
  4. Communication: Communication is key. Things run smoother when all levels share results and make everyone feel part of the team.
  5. Trash the Busy Work: People perform better when they feel useful. Saddling citizen data scientists with a bunch of busy work that never gets used is a one-way road to burnout — and thus a high turnover rate. Try to utilize every citizen data scientist to their highest ability.

Implementing a Comprehensive Data Team

Advancements in machine learning have democratized the information industry, allowing small businesses to harness the power of big data.

But if you’re not a large corporation or enterprise — or even if you are — hiring a full complement of expert and citizen data scientists may not be a budgetary possibility.

That’s where data analysis software and tools — like Inzata Analytics — step in and save the day. Our end-to-end platform can handle all your modeling, analytics, and transformation needs for a fraction of the cost of adding headcount to your in-house crew or extensive tech stacks. Let’s talk about your data needs. Get in touch today to kick off the conversation. If you want your business to profit as much as possible, then leveraging data intelligence systems is the place to start.

Business Intelligence

The Beginner’s Guide to SQL for Data Analysis

What Is SQL?

SQL stands for “Structured Query Language,” and it’s the programming protocol used for relational database management systems. Or, in plain English, SQL is the code that accesses and extracts information from data sets.

The Importance of SQL and Data Analysis

In our current economy, data ranks among the most commodifiable assets. It’s the fuel that keeps social media platforms profitable and the digital mana that drives behavioral marketing. As such, crafting the best SQL data queries is a top priority. After all, they directly affect bottom lines.

In our examples below, we use the wildcard * liberally. That’s just for ease and simplicity. In practice, wildcards should be used sparingly and only at the end of the query condition.

Display a Table

It’s often necessary to display tables on websites, internal apps, and reports.

In the examples below, we show how to a) pull every column and record from a table and b) pull specific fields from a table.


Adding Comments

Adding comments to SQL scripts is important, and if multiple people are working on a project, it’s polite! To add them, simply insert two dashes before the note. Don’t use punctuation in comments, as it could create querying problems.

Below is an example of a comment in a SQL query.


Combine Columns

You’ll want to combine two columns into one for reporting or output tables.

In our example below we’re combining the vegetable and meat columns from the menu table into a new field called food.


Display a Finite Amount of Records From a Table

Limiting the number of records a query returns is standard practice.

In the example below, we’re pulling all the fields from a given table and limiting the output to 10 records.


Joining Tables Using INNER JOIN

The INNER JOIN command selects records with matching values in both tables.

In the example below, we’re comparing the author and book tables by using author IDs. This SQL query would pull all the records where an author’s ID matches the author_ID fields in the book table.


Joining Tables Using LEFT JOIN

The LEFT JOIN command returns all records from the left table — in our example below that’s the authors table — and the matching records from the right table, or the orders table.


Joining Tables Using RIGHT JOIN

The RIGHT JOIN command returns all records from the right table — in our example the orders table — and the matching records from the left table, or the authors.


Joining Tables Using FULL OUTER JOIN

The FULL OUTER JOIN command returns records when there’s a match in the left table, which is the authors table in our example, or the right table — the orders table below. You can also add a condition to further refine the query.


Matching Part of a String

Sometimes, when crafting SQL queries, you’ll want to pull all the records where one field partially meets a certain criteria. In our example, we’re looking for all the people in the database with an “adison” in their first names. The query would return every Madison, Adison, Zadison, and Adisonal in the data set.


If/Then CASE Logic

Think of CASE as the if/then operator for SQL. Basically, it cycles through the conditions and returns a value when a row matches. If a row doesn’t meet any of the conditions, the ELSE clause is activated.

In our example below, a new column called GeneralCategory is created that indicates if a book falls under the fiction, non-fiction, or open categories.



The HAVING and WHERE keywords accomplish very similar tasks in SQL. However, WHERE is processed before a GROUP BY command. HAVING, conversely, is processed after a GROUP BY command.

In our example below, we’re pulling the number of customers for each store, but only including stores with more than 10 customers.


It’s fair to argue that SQL querying serves as the spine of the digital economy. It’s a valuable professional asset, and taking time to enhance your skills is well worth the effort.

Data Preparation Data Quality

Cleaning Your Dirty Data: Top 6 Strategies

Cleaning data is essential to making sure that data science projects are executed with the highest level of accuracy possible. Manual cleaning calls for extensive work, though, and it also can induce human errors along the way. For this reason, automated solutions, often based on basic statistical models, are used to eliminate flawed entries. It’s a good idea, though, to develop some understanding of the top strategies for dealing with the job.

Pattern Matching

A lot of undesirable data can be cleaned up using common pattern-matching techniques. The standard tool for the job is usually a programming language that handles regular expressions well. Done right, a single line of code should serve the purpose well.

1) Cleaning out and fixing characters is almost always the first step in data cleaning. This usually entails removing unnecessary spaces, HTML entity characters and other elements that might interfere with machine or human reading. Many languages and spreadsheet applications have TRIM functions that can rapidly eliminate bad spaces, and regular expressions and built-in functions usually will do the rest.

2) Duplicate removal is a little trickier because it’s critical to make sure you’re only removing true duplicates. Using other good data management techniques will make duplicate removal simpler, such as indexing. Near-duplicates, though, can be tricky, especially if the original data entry was performed sloppily.

Efficiency Improvement

While we tend to think of data cleaning as mostly preparing information for use, it also is helpful in improving efficiency. Storage and processing efficiency are both ripe areas for improvement.

3) Converting fields makes a big difference sometimes to storage. If you’ve imported numerical fields, for example, and they all appear in text columns, you’ll likely benefit from turning those columns into integers, decimals or floats.

4) Reducing processing overhead is also a good choice. A project may only require a certain level of decimal precision, and rounding off numbers and storing them in smaller memory spaces can speed things up significantly. Just make sure you’re not kneecapping required decimal precision when you use this approach.

Statistical Solutions

Folks in the stats world have been trying to find ways to improve data quality for decades. Many of their techniques are ideal for data cleaning, too.

5) Outlier removal and the use of limits are common ways to analyze a dataset and determine what doesn’t belong. By analyzing a dataset for extreme and rare data points, you can quickly pick out what might be questionable data. Be careful, though, to recheck your data afterward to verify that low-quality data was removed rather than data about exceptional outcomes.

Limiting factors also make for excellent filters. If you know it’s impossible for an entry to register a zero, for example, installing a limit above that mark can eliminate times when a data source simply returned a blank.

6) Validation models are useful for verifying that your data hasn’t been messed up by all the manipulation. If you see validation numbers that scream that something has gone wrong, you can go back through your data cleaning process to identify what might have misfired.

Big Data Data Preparation

6 Core Principles Behind Data Wrangling

What Is Data Wrangling? 

What is data wrangling? Also known as data cleaning, data remediation, and data munging, data wrangling is the digital art of molding and classifying raw information objects into usable formats. Practitioners use various tools and methods — both manual and automated — but approaches vary from project to project depending on the setup, goal, and parameters.

Why Is Data Wrangling Important?

It may sound cliche, but it’s true: data is the gold of today’s digital economy. The more demographic information a company can compile about extant customers and potential buyers, the better it can craft its marketing campaigns and product offerings. In the end, quality data will boost the company’s bottom line.

However, not all data is created equal. Moreover, by definition, informational products can only be as good as the data upon which they were built. In other words, if bad data goes in, then bad data comes out.

What Are the Goals of Data Wrangling?

Data wrangling done right produces timely, detailed information wrapped in an accessible format. Typically, businesses and organizations use wrangled data to glean invaluable insights and craft decision frameworks.

What Are the Six Core Steps of Data Wrangling?

The data remediation scaffolding consists of six pillars: discovery, structuring, cleaning, enriching, validating, and publishing.


Before implementing improvements, the current system must be dissected and studied. This stage is called the discovery period, and it can take anywhere from a few days to a few months. During the discovery phase, engineers unearth patterns and wrap their heads around the best way to set up the system.


After you know what you are working with, the structuring phase begins. During this time, data specialists create systems and protocols to mold the raw data into usable formats. They also code paths to distribute the information uniformly. 


Analyzing incomplete and inaccurate data can do more harm than good. So next up is cleaning. This step mainly involves scrubbing incoming information of null values and extinguishing redundancies. 


Companies may use the same data, but what they do with it differs significantly. During the enriching step of a data wrangling process, proprietary information is added to objects, making them more useful. For example, department codes and meta information informed by market research initiatives may be amended to each object.


Testing — or validating — is the backbone of all well-executed data systems. During this phase, engineers double-check to ensure the structuring, cleaning, and enriching stages were processed as expected. Security issues are also addressed during validation.


The end product of data wrangling is publication. If the information is headed to internal departments or data clients, it’s typically deployed through databases and reporting mechanisms. If the data is meant for promotional materials, then copywriting, marketing, and public relations professionals will likely massage the information into relatable content that tells a compelling story. 

Data Wrangling Examples

We’ve discussed the ins and outs of data wrangling procedures; now, let’s review common examples. Data wranglers typically spend their days:

  • Finding data gaps and deciding how to handle them
  • Analyzing notable outliers in the data and deciding what to do about them
  • Merging raw data into a single database or data warehouse
  • Scrubbing irrelevant and unnecessary information from raw data

Are you in need of a skilled data wrangler? The development of AI-powered platforms, such as Inzata Analytics, has rapidly expedited the process of cleaning and wrangling data. As a result, professionals save hours on necessary tasks that can transform your data landscape and jump-start profits.

Business Intelligence Data Analytics

Can Decision Intelligence Drive Your Analytics Strategy?

The earliest forms of decision intelligence emerged around 2012. Since then, decision intelligence technology has gained traction in data science and product management fields. Ultimately, this type of technology has quite a lot to offer in many professional organizations, given the monumental amount of data we have at our disposal. We’ve gathered all the information you need to help you understand what decision intelligence is and how it can help business professionals streamline their workflow across multiple industries. 

What Is Decision Intelligence?

Before we jump into how organizations use decision intelligence as a part of their daily work, you need to understand the fundamentals of decision intelligence. At its core, decision intelligence focuses on utilizing machine learning and data analytics to help professionals make important business decisions. Decisions, in this instance, often consist of irrevocable resource allocation or strategic actions that have undeniable and irreversible consequences. Therefore, when a stakeholder is responsible for making a decision, ensuring they make the correct definitive decision is essential. 

Decision intelligence takes advantage of the availability of machine learning and an abundance of available data to analyze circumstances, find patterns and predict outcomes. While many individuals may claim that data scientists can do just that, there’s one key difference: the AI utilized in decision intelligence operations focuses on the facts, statistics, patterns, and expected outcomes. 

Finally, it’s important to note that there are many different forms of decision intelligence techniques, including but not limited to: 

  • Decision management
  • Agent-based decision systems
  • Descriptive analysis
  • Decision support
  • Diagnostic and predictive analysis

Ultimately, decision intelligence exists to help stakeholders understand the potential outcomes of key decisions made during various project stages. 

Why Is Intelligence Analysis Important?

Decision intelligence has continued to grow over the last decade and will continue to develop. Industry experts believe that these tools will be available in regular consumer software suites like Microsoft Office in the years to come. Therefore, it becomes obvious that there’s a need for these tools, but why? 

To put it simply, one of the biggest problems with human decision-making involves the inability to see real-world results from all angles. For instance, decision intelligence empowers businesses to automate some parts of the decision-making process using machine learning and data-driven observations. 

When a business takes advantage of intelligent analysis and decision-making, they’re likely to experience many benefits. Some of these benefits include: 

  • Faster response time to disruptions
  • More accurate decision-making
  • Developed framework for long-term effects of immediate decisive action
  • Reduced risk as a result of poor decision-making
  • Improved ROI on many projects as a result of faster turn-around times

These are just a few of the benefits that decision intelligence offers. Managers of organizations looking to optimize and improve their workflow need to take advantage of decision intelligence to reduce project risks effectively. 

How Does Analytics Play A Role?

Analytics is one of the primary aspects of utilizing decision intelligence, AI, and artificial decision-making. Data scientists often review data and draw logical conclusions based on the data at hand. However, decision intelligence can take this process to the next step. 

Many organizations already store and utilize a large amount of data on internal servers. Decision intelligence streamlines the process of reviewing, analyzing, and drawing conclusions from the data gathered, presenting logical conclusions based on the data provided. 

Because so many organizations already store data, much of which goes unused for large periods, decision intelligence has many applications. Organizations can streamline the analytical process and spend more time seeing the results of improved decision-making abilities.


Overall, many businesses now rely on decision intelligence frameworks for automating the decision-making process. By understanding the effects of artificial intelligence, intelligent analysis, and logical decision-making, stakeholders can take advantage of machine learning to directly improve their workflows. While the concept of decision intelligence may still be relatively new, it has already been shown to provide businesses with the power they need to overcome obstacles and make effective decisions on a consistent basis.

Data Preparation Data Quality

Content Tagging: How to Deal With Video and Unstructured Data

Working with unstructured video data can be extremely difficult to tame! But don’t worry. With a few handy tips, the process becomes a lot more manageable.

Please note: this is our second article in a series on unstructured data. Click here to read the first installment, which explores indexing and metadata.

What Is the Problem With Unstructured Data?

Unstructured information is an unwieldy hodgepodge of graphic, audio, video, sensory, and text data. To squeeze value from the mess, you must inspect, scrub, and sort the file objects before feeding them to databases and warehouses. After all, raw data is of little use if it cannot be adequately leveraged and analyzed.

What Is Content Tagging?

In the realm of information management, content tagging refers to the taxonomic structure established by an organization or group to label and sort raw data. You can think of it as added metadata.

Content tagging is largely a manual process. In a typical environment, people examine the individual raw files and prep them for data entry. Common tasks include:

  • Naming each item
  • Adding meta descriptions of images and videos
  • Splicing videos into frames
  • Separating and marking different mediums

How to Use Content Tagging to Sort Unstructured Data

You can approach content tagging in several ways. Though much of the work is best done manually, there are also ways to automate some processes. For example, if an incoming file ends with a .mov or .mp4 suffix, you can write a script that automatically tags it as a video. The same can be done for graphics and text documents.

Tagging helps organize unstructured data as it provides readable context atop which queries can be crafted. It also allows for pattern establishment and recognition. In fact, photo recognition programs are, in large part, fueled by extensive tagging.

The Pros and Cons of Content Tagging

Tagging has its pros and cons. The downside is the manual labor involved. Depending on the amount of inbound data, it could take considerable resources to get the job done. Many businesses prefer to enlist third-party database management teams to mitigate costs and free up personnel.

As for pros, there are a couple. Firstly, content tagging makes data organization much more manageable. When you label, sorting becomes a snap. Secondly, tagging adds more value to data objects, which allows for better analysis.

Let’s Transform Your Unstructured Data

Leveraging AI-powered tools to perform complex data management tasks can save you money and increase efficiency in the long run. Inzata Analytics maintains a team of experts that focuses on digital data, analytics, and reporting. We help businesses, non-profits, and governments leverage information technology to increase efficiency and profits.

Get in touch. Let’s talk. We can walk you through the advantages of AI-powered data management and how it can boost your bottom line. See a demo of the platform here.

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