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What is Data Integrity & Why is it Important in Data Analytics

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What is Data Integrity?

Data integrity is the measure of accuracy, consistency, and completeness of an organization’s data. This also includes the level of trust the organization places on its data’s validity and veracity throughout its entire life cycle.

As a core component of data management and data security, data integrity revolves around who has access to the data, who is able to make changes, how it’s collected, inputted, transferred, and ultimately how it’s maintained over the course of its life.

Companies are subject to guidelines and regulations from governing organizations such as the GDPR to maintain certain data integrity best practices. Requirements are particularly critical for companies in the healthcare and pharmaceutical industry but remain important to decision-making across all sectors. 

Why is Data Integrity Important?

Data integrity is important for a number of reasons, key factors include:

  • Data Reliability & Accuracy – Reliable and accurate data is key to driving effective decision-making. This also assists employees in establishing trust and confidence in their data when making pivotal business decisions.
  • Improving Reusability – Data integrity is important to ensure the current and future use of an organization’s data. Data can be more easily tracked, discovered, and reused when strong integrity is maintained.
  • Minimizing Risks – Maintaining a high level of integrity can also minimize the dangers and common risks associated with compromised data. This includes things such as the loss or alteration of sensitive data.

Risks of Data Integrity

If data integrity is important to mitigating risks, what risks are involved? 

Many companies struggle with challenges that can weaken one’s data integrity and cause additional inefficiencies. Some of the most common risks to be aware of are the following:

  • Human Error – Mistakes are bound to happen, whether they be intentional or unintentional. These errors can occur when proper standards are not followed, if the information is recorded or inputted incorrectly, or in the process of transferring data between systems. While this list is not exhaustive, all of these are able to put the integrity of an organization’s data at risk.
  • Transfer Errors – Transferring data from one location to another is no small task, leaving room for possible errors during the transfer process. This process can result in altering the data and other table inaccuracies.
  • Hardware Problems – Though technology has come a long way by the means of hardware, compromised hardware still poses a risk to data integrity. Compromised hardware can cause problems such as limited access to data or loss of the data entirely.

Data Integrity vs. Data Quality

Are data integrity and data quality the same thing? No, despite their similar definitions and joint focus on data accuracy and consistency, data integrity and data quality are not one and the same.

Data quality is merely one component of data integrity as a whole. Integrity stems beyond whether the data is both accurate and reliable and instead also governs how data is recorded, stored, transferred, and so on. This extension of components, particularly when it comes to the additional context surrounding the data’s lifespan, is where the primary distinction between the two lies.

To sum up, data integrity plays a deciding role in ensuring accurate data that can be easily discovered, maintained, and traced back to its original data source.

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The Data-Driven Difference Between Applying & Consuming Data

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Organizations across the board have recognized the significance of using data to drive decision-making and grow their operations. 94% of enterprises say data and analytics are important to their business growth and decision-making process. 

Due to the fundamental role analytics plays in enterprises today, demand for the presence of data in any and all business activities has also developed. Dashboards and reports have become an essential aspect of meetings and day-to-day operations. Whether it be used to address broader strategic problems or to support upcoming project decisions, there is an ever-present need for data to be involved in some capacity. 

However, just because graphs and data visualizations have become the new standard in the workplace doesn’t necessarily mean companies are actually applying the information. The presence of data does not automatically equate to being data-driven. This leads us to the all-important question: Are you applying data or just consuming it?

Using Data to Fit a Narrative

To begin, the problem that’s holding companies back from becoming truly data-driven is that many use data to fit a narrative. More often than not data is used as a means of providing evidence to support predetermined conclusions. This means centering data efforts around backing up ideas or gut feelings rather than focusing on what the data actually tells.

Coming to conclusions before exploring the data can be a recipe for disaster. However, this occurs in businesses today more often than you’d think. Everyone has their own agenda as well as objectives and goals they are responsible for hitting. Even though business leaders are able to recognize the importance of data, the data might not always align with their plan of action. 

Not Putting Biases to the Test

Similarly, not putting these biases to the test is another obstacle holding businesses back from maximizing the value of their analytics. Ronald Coase, the renowned British economist, once said “if you torture the data long enough, it will confess anything.” This quote describes the ease of manipulating data and imposing personal biases, whether they be intentional or unintentional, on the process of data analysis. 

While intuition is important in business, being data-driven is about putting those biases to the test, exploring the data, diving deeper than the surface, and uncovering insights that may have not been considered otherwise. 

How to Become Data-Driven

So how do you make the most of your data? What does it take to become data-driven? Being data-driven doesn’t mean solely investing in the newest data analytics tools or having the highest quality data possible. A data-driven culture is what allows your data to guide you, with the help of technology and governance, in the right direction. An organization’s culture is where the divide between consuming data versus actually applying it comes into play. Here are some key steps to keep in mind when on the path to becoming data-driven.

Improve Accessibility to Data

The initial core element of becoming data-driven is having readily available access to quality data that can be used for analysis. After all, how can any value be derived from your data if no one is able to access the information they need in a timely and efficient manner? Or worse, if the data still needs to be cleansed prior to use. These are all factors that impact the ease of use and flexibility when it comes to using data to drive decisions. Implementing a robust data governance strategy is the key to maintaining the quality and accessibility of your data.

To access your data’s accessibility and current governance strategy, start by asking the following questions:

  • How do you manage and store your data?
  • How do you access your company’s data?
  • Who has access to the data?
  • What metrics are you using to measure data quality?

Build Data Literacy 

Furthermore, data can’t produce any type of meaningful value if no one in your organization is able to understand it. Provide training and opportunities for all employees, beyond the data science and analytics teams, to develop their understanding of how to read, interpret, and analyze data. This will allow for more fluid communication and accessibility to insights across every department.

Promote Exploration & Curiosity

For data to have a meaningful impact on business decision-making, teams have to be willing to probe the data and continually ask it questions. Not every issue or insight can be seen from the surface, deep dives and exploration are required to uncover information that may have not been discovered with basic analysis. Implementing a weekly brainstorming discussion or providing access to further educational training can lead to better engagement amongst employees as well as higher quality insights.  

Communicate High-Level Goals 

Communication of high-level goals is critical to understanding what the organization is trying to achieve through these changes. It’s important to foster a common understanding of how data should be used and prioritized in the broader scope of the company’s goals. This will not only ensure everyone is on the same page, but it will also communicate the business value of data to those involved. 

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Why We Build Data Warehouses

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What is a Data Warehouse?

A data warehouse is where an organization stores all of its data collected from disparate sources and various business systems in one centralized source. This aggregation of data allows for easy analysis and reporting with the ultimate end goal of making informed business decisions.

While data from multiple sources is stored within the warehouse, data warehouses remain separate from operational and transactional systems. Data flows from these systems and is cleansed through the ETL process before entering the warehouse. This ensures the data, regardless of its source, is in the same format which improves the overall quality of data used for analysis as a result.

There are many additional advantages to implementing a data warehouse. Some key benefits of data warehouses include the following:

  • Enhanced business intelligence and reporting capabilities
  • Improved standardization and consistency of data
  • Centralized storage increases accessibility to data
  • Better performance across systems
  • Reduced cost of data management

Why is a Data Warehouse Important?

Data warehouses are important in that they increase flexible access to data as well as provide a centralized location for data from disparate sources.

With the rapidly increasing amounts of operational data being created each day, finding the data you need is half the battle. You’re likely using multiple applications and collecting data from a number of sources. Each of these sources recording data in its own unique format.

Say you want to figure out why you sold a higher volume of goods in one region compared to another last quarter. Traditionally, you would need to find data from your sales, marketing, and ERP systems. But how can you be certain this information is up to date? Do you have access to each of these individual sources? How can you bring this data together in order to even begin analyzing it?

These questions depict how a simple query can quickly become an increasingly time consuming and complex process without the proper infrastructure. Data warehouses allow you to review and analyze all of this data in one unified place, developing a single source of data truth in your organization. A single query engine is able to present data from multiple sources, making accessibility to data from disparate sources increasingly flexible.

Why We Build Data Warehouses

At the end of the day, data warehouses help companies answer questions. What types of employees are hitting their sales targets? Which customer demographics are most likely to cancel their subscription? Why are we selling more through partnerships and affiliates compared to email marketing? 

Questions like these arise by the handful throughout the course of everyday business. Companies need to be able to answer these questions fast in order to quickly respond to change. Data warehouses empower businesses with the answers they need, when they need them.

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Big Data Business Intelligence Data Analytics

Data Storytelling: The Essential Skill for the Future of Analytics

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Collecting data and performing analysis doesn’t mean much if you can’t find a way to effectively convey its meaning to an audience. Oftentimes, audience members aren’t well-positioned to understand analysis or to critically think about its implications. To engage with an audience, you need to embrace storytelling. Let’s take a look at what that means when talking about storytelling with data.

How to Build a Story Arc

One of the simplest ways to approach the problem is to treat your story as a three-act play. That means your story will have:

  • An introduction
  • A middle
  • A conclusion

Each section of the story needs to be delineated so the audience understands the structure and the promise of a story that comes with it.

What Goes into an Introduction

In most cases, data is hidden before being subjected to analysis. That means you have to set the scene, giving the audience a sense of why the data is hidden and where it came from. You don’t necessarily want to jump right to conclusions about the data or even any basic assumptions. Instead, the data should be depicted as something of a mysterious character being introduced.

If the storytelling medium is entirely visual, then you need to find a way to present the data. The Minard Map is a classic example of how to do this. It uses data to tell the story of the slow destruction of Napoleon’s army during the invasion of Russia. Minard employs a handful of vital statistics to explain what’s going to happen as the story unfolds. These include the:

  • Sizes of the competing armies
  • The geographic proximity of the two forces 
  • Air temperature
  • Rainfall

The audience can familiarize themselves with the data quickly and easily understand what this story is going to entail just by reading the vital statistics. In this particular case, this story is going to be about man versus the elements.

Unfolding the Middle of the Story

Following the presentation of the story should guide the audience toward the conclusion. In the case of the Minard Map, the middle of the story is about a slowly shrinking French army and a slowly growing Russian army that tracks the French. Military engagements occur, and the weather starts to turn. Geographic elements are worked into the graph, too, as the armies cross rivers and march into towns.

Providing the Conclusion

A well-executed data visualization should let the audience get to the conclusion without much prodding. The Minard Map makes its point without beating the audience over the head. By the third act, it’s clear that the conditions have turned and the Russians are now close to matching the French in manpower. As the two armies reach Moscow, it’s clear that what started as a triumphant march has ended as an immense loss.

In its best form, data storytelling shouldn’t feel like a sea of numbers at all. People have seen numerous charts and graphs in their lifetimes, even over the regular course of a single day of business, and that means good-enough visualizations that are focused on presenting numbers tend to become white noise.

Takeaways

Good data storytellers make history. Florence Nightingale’s analysis of casualties during the Crimean War permanently changed the way all forms of medical treatment are provided. Her work is still required reading at many nursing and medical schools more than 150 years later. That’s the goal: to engage the audience so thoroughly that the story and the data long outlast your initial presentation.

Accomplishing that goal requires planning. You can’t just fire up your best data visualization software, import some info from Excel and let the bars and bubbles fly. That’s easy to do because many software packages can deliver solid-looking results in a matter of minutes.

Top-quality data storytelling occurs when the audience is given just enough information to set and understand the scene. Someone scanning the visualizations will then follow the information as it unfolds over time. As the audience approaches the conclusion, they should be left with a strong impression regarding what the data says and what they should learn from it.

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Big Data Data Analytics Data Quality

The Fundamentals of Mastering Metadata Management

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Poor data quality is estimated to cost organizations an average of $12.8 million per year. All methods of data governance are vital to combating this rising expense. While metadata has always been recognized as a critical aspect of an organization’s data governance strategy, it’s never attracted as much attention as flashy buzzwords such as artificial intelligence or augmented analytics. Metadata has previously been viewed as boring but inarguably essential. With the increasing complexity of data volumes, though, metadata management is now on the rise. 

According to Gartner’s recent predictions for 2024, organizations that use active metadata to enrich their data will reduce time to integrated data by 50% and increase the productivity of their data teams by 20%. Let’s take a deeper look into the importance of metadata management and its critical factors for an organization.

What is Metadata?

Metadata is data that summarizes information about other data. In even shorter terms, metadata is data about other data. While this might sound like some form of data inception, metadata is vital to an organization’s understanding of the data itself and the ease of search when looking for specific information. 

Think of metadata as the answer to the who, what, when, where, and why behind an organization’s data. When was this data created? Where did this data come from? Who is using this data? Why are we continuing to store this information?

There are many types of metadata, these are helpful when it comes to searching for information through various key identifiers. The two primary forms of metadata include:

  • Structural – This form of metadata refers to how the information is structured and organized. Structural metadata is key to determining the relationship between components and how they are stored.
  • Descriptive – This is the type of data that presents detailed information on the contents of data. If you were looking for a particular book or research paper, for example, this would be information details such as the title, author name, and published date. Descriptive metadata is the data that’s used to search and locate desired resources.
  • Administrative – Administrative metadata’s purpose is to help determine how the data should be managed. This metadata details the technical aspects that assist in managing the data. This form of data will indicate things such as file type, how it was created, and who has access to it. 

What is Metadata Management?

Metadata management is how metadata and its various forms are managed through processes, administrative rules, and systems to improve the efficiency and accessibility of information. This form of management is what allows data to easily be tracked and defined across organizations.

Why is Metadata Management Important?

Data is becoming increasingly complex with the continually rising volumes of information today. This complexity highlights the need for robust data governance practices in order to maximize the value of data assets and minimize risks associated with organizational efficiency.

Metadata management is significant to any data governance strategy for a number of reasons, key benefits of implementing metadata processes include:

  • Lowered costs associated with managing data
  • Increases ease of access and discovery of specific data
  • Better understanding of data lineage and data heritage
  • Faster data integration and IT productivity

Where is this data coming from?

Show me the data! Not only does metadata management assist with data discovery, but it also helps companies determine the source of their data and where it ultimately came from. Metadata also makes tracking of alterations and changes to data easier to see. Altering sourcing strategies or individual tables can have significant impacts on reports created downstream. When using data to drive a major company decision or a new strategy, executives are inevitably going to ask where the numbers are coming from. Metadata management is what directs the breadcrumb trail back to the source. 

With hundreds of reports and data volumes constantly increasing, it can be extremely difficult to locate this type of information amongst what seems to be an organizational sea of data. Without the proper tools and management practices in place, answering these types of questions can seem like searching for the data needle in a haystack. This illuminates the importance of metadata management in an organization’s data governance strategy.

Metadata Management vs. Master Data Management

This practice of managing data is not to be confused with Master Data Management. The two have similar end goals in mind when it comes to improving the capability and administration of digital assets. But managing data is not all one and the same, the practices are different through their approaches and structural goals. Master data management is more technically weighted to streamline the integration of data systems while metadata management focuses on simplifying the use and access of data across systems.

Overview

Metadata management is by no means new to the data landscape. Each organization’s use case of metadata will vary and evolve over time but the point of proper management remains the same. With greater data volumes being collected by companies than ever before, metadata is becoming more and more critical to managing data in an organized and structured way, hence its rising importance to one’s data management strategy.

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Big Data Business Intelligence Data Analytics

3 Things Companies Are Doing Right Now to Bounce Back

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The past year has been filled with a long list of unforeseen challenges for all organizations. This uncertainty has left businesses with more and more questions that need to be answered, particularly when it comes to their strategies moving forward. 

There’s no question that data and analytics are paving the way for businesses in the post-pandemic world, but what exactly are companies doing right now to bounce back? Let’s take a closer look at what leading companies are doing to get back on track and prepare for growth in a post-COVID world.

Investing in the Digital Future

There has always been an enormous amount of talk surrounding the need for digital transformation. Organizations have long used the buzzword loosely when discussing their strategy and high-level goals. For many years, there has been a recognized need for these digital efforts but the transformation has been slow to move. The recent market disruptions, though, have catalyzed digital transformation and emphasized the importance of cost optimization and process improvement through new digital strategies. 

Industry leaders are doubling down on their investment in digital strategy and IT implementation. According to a recent Gartner survey, 69% of board of directors have accelerated their digital business initiatives due to COVID’s disruption. This has caused an increase in “forward-looking investments” that will aid in quick responses to unexpected events. Through widespread digital transformation, there has been an apparent shift towards preparedness and the improved agility of organizations as a whole. 

Additionally, digital transformation opens doors for things like customer engagement due to increased customer visibility and opportunities for personalization. Many companies are entirely transforming their business model while expanding their digital product offerings as a means for revenue growth. 

To start accessing your own digital strategy, start by asking questions such as:

  • How is your digital investment aligned with your business goals?
  • What metrics and KPIs need to be tracked to effectively measure change?
  • What strategies and high-level goals should be understood at all levels of the organization?

Data-Driven Decision Making

Decisions, decisions, decisions. The one thing you can always count on to remain constant in business, regardless of any change and uncertainty in the business environment. The road following a disruption, though, is filled with new questions surrounding customer behaviors and strategic decisions that need to be made quickly. 

The recent marketplace changes have highlighted the need for rapid decision-making. Rapid decision-making falls into both an organization’s preparedness and ability to adapt to evolving situations. Using data to drive and inform decision-making is no longer considered a competitive advantage, but instead is identified as a need in order to compete. Whether you’re predicting staff and inventory requirements or evaluating online buying behaviors through customer analytics, data should be at the core.

Not Putting Things Off

Arguably the most important thing companies are doing to bounce back from the downturn is taking action. The time is now, stop putting things off! The vast majority of companies deferred a number of initiatives and business development efforts due to COVID. You’ve likely heard it used as an excuse to delay or revisit a project in the future yourself. 

Nevertheless, progress or effective change never came from putting something on the back burner. You can’t expect growth to happen on its own and continue to delay efforts until things go back to what was once considered normal. Reevaluate your priorities, preparedness, and strategies from the individual level all the way up to the overarching organization. 

Review

To sum up, the points stated above, the key to moving forward is all about adapting to changing situations. Whether it’s your ability to quickly generate insights that will drive decision-making or investing in new digital channels, it all comes back to how prepared you are to respond and adapt to change.

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7 Bad Habits Every Data Scientist Should Avoid

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Are you making these mistakes? As a data scientist, it can be easy to fall into some common traps. Let’s take a look at the most common bad habits amongst data scientists and some solutions on how to avoid them. 

1. Not Understanding the Problem

Ironically, for many data scientists, understanding the problem at hand is the problem itself. The confusion here often occurs for a couple of reasons. Either there is a disconnect between the data scientist’s perspective and the business context of the situation or the instructions given are very vague and ambiguous. These reasons all lead back to a lack of information and understanding of the situation.

Misunderstandings of the business case can lead to wasted time spent working towards the wrong approach and often causes many unnecessary headaches. Don’t be afraid to ask clarifying questions, having a clear picture of the business problem being asked is vital to your efficiency and effectiveness as a data scientist. 

2. Not Getting to Know Your Data

We’re all guilty of wanting to jump right in and get the ball rolling, especially when it comes to a shiny new project. This ties into the last behavioral point, rushing to model your data without fully understanding its contents can create numerous problems in itself. A thorough and precise exploration of the data prior to analysis can help determine the best approach to solving the overarching problem. As tempting as it may be, it’s important to walk before you can run.  

After all, whatever happened to taking things slow? Allocate time for yourself early on to conduct an initial deep dive. Don’t skip over the getting to know you phase and jump right into bed with the first model you see fit. It might seem counterintuitive but taking time to get to know your data at the beginning can help save time and increase your efficiency later down the line. 

3. Overcomplicating Your Model

Undoubtedly, you will face numerous challenges as a data scientist, but you will quickly learn that a fancy and complicated model is not a one size fits all solution. It’s common for a complex model to be a data scientists’ first choice when diving into a new project. The bad habit, in this case, is starting with the most complex model when a more simple solution is available. 

Try starting with the most basic approach to a problem and expand your model from there. Don’t overcomplicate things, you could be causing yourself an additional headache with the time drained into the more intricate solution.

4. Going Straight for the Black Box Model

What’s worse than diving in headfirst with an overly complex model? Diving in headfirst with a complex model you don’t entirely understand. 

Typically, a black box is what a data scientist uses to deliver outputs or deliverables without any knowledge of how the algorithm or model actually works. This happens more often than one might think. Though this may be able to produce effective deliverables, it can also lead to increased risk and additional problems. Therefore, you should always be able to answer the question of “what’s in the box?” 

5. Always Going Where No One Has Gone Before

Unlike the famous Star Trek line, you don’t always have to boldly go where no man has gone before in the realm of data science. While being explorative and naturally curious when it comes to the data is key to your success, you will save a lot of time and energy in some cases by working off of what’s already been done.

Not every model or hypothesis has to be a groundbreaking, one of a kind idea. Work from methods and models that other leaders have seen success with. Chances are that the business questions you’re asking your data or the model you’re attempting to build have been done before. 

Try reading case studies or blog posts speaking on the implementation of specific data science projects. Becoming familiar with established methods can also give you inspiration for an entirely new approach or lead you to ideas surrounding process improvement.

6. Doing It All Yourself

It’s easy to get caught up in your own world of projects and responsibilities. It’s important, though, to make the most of the resources available to you. This includes your team and others at your organization. Even your professional network is at your disposal when it comes to collecting feedback and gaining different perspectives. 

If you find yourself stuck on a particular problem, don’t hesitate to involve key stakeholders or those around you. You could be missing out on additional information that will help you to better address the business question at hand. You’re part of a team for a reason, don’t always try to go it alone!

7. Not Explaining Your Methods

The back end of data science projects might be completely foreign to the executive you’re working within marketing or sales. However, this doesn’t mean you should just brush over your assumptions and process to these non-technical stakeholders. You need to be able to explain how you got from point A to point B, how you built your model, and how you ultimately produced your final insights in a way that anyone can understand.

Communication is essential to ensure the business value is understood and properly addressed from a technical standpoint. Though it might be difficult to break things down in a way that non-technical stakeholders can understand, it’s important to the overall success of any project you will work on. This is where storytelling tactics and visualizations can come in handy and easily allow you to communicate your methods.

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How to Become the BI Rookie of the Year

With the new year upon us and new opportunities at hand, it’s time to get your head in the game. There’s a business intelligence all-star in us all. Whether you’re new to the business intelligence league, have recently been transferred to a new team, or are just looking to up your data game, here are some strategies that can help.

Practice Like You Play

While practice might not always make perfect, it helps you get a little closer in your continuous pursuit. Natural talent can only take you so far, making practice essential to developing skills and gaining experience. Try taking on an additional practice project dedicated to advancing your technical skills. This will ensure your improvement over time and allow you to experiment outside of the common workplace parameters of traditional methods and time constraints. 

You should begin by picking out your “play data.” However, it’s important to note that this shouldn’t be just any old data. Find data related to something that interests you, take inspiration from any personal hobbies you may have. This could range from anything such as your music listening activity, your local weather data, or even your fantasy football league. 

Practicing your skills with data you have a personal invested interest in will give you the opportunity to play and experiment with new techniques. This will also increase your chances of continuing these development efforts. These projects are also a great way to demonstrate your curiosity when it comes to data, which can be key when looking to get drafted by another organization.  

If you’re stuck on sparking an initial idea, try researching open-source data and see if anything catches your eye. Resources like the Registry of Open Data on AWS or the U.S. government’s open data library are great places to start.

You can also see Inzata’s guide on Where to Get Free Public Datasets for Data Analytics Experimentation.

Learn From the Pros

Regardless of how much time you’ve spent in the league so far, there will always be more to learn from those around you. Taking the student approach or possessing the rookie mindset is vital to continuous learning. 

Start by looking to those who have demonstrated success in their field. This can be anyone from the higher-ups within your organization to an industry influencer. You can develop these connections by asking an executive to lunch or joining various discussion forums and networking groups.

Additionally, try attending as many training’s, webinars, and conferences as you can. These virtual events are more accessible than ever due to the recent widespread transition to remote work, giving you access to thought leaders across the globe.

There is also an abundance of available content online such as books and online courses. These resources will give you instant access to decades of industry experience and valuable lessons learned through hands-on accounts.

What’s Your Next Home Run?

Your batting average when it comes to tasks and projects is crucial to long term success. You might get lucky every once in a while if you’re aimlessly swinging for the fences. It’s important, though, to make sure you are establishing attainable goals for yourself. You can think of these goals as the next home run you’re looking to hit. Having a clear vision of professional milestones will help guide you in the right direction and ultimately increase your chances of achievement. 

Studies show that you’re 42% more likely to achieve your goals if you simply write them down.

One strategy that will improve your batting average is to determine SMART goals for yourself in terms of your role and what you’d like to achieve. Now, these goals aren’t just smart in the traditional sense of the word. 

SMART is an acronym for:

  • Specific – Make your goals clear and concise. Don’t leave any room for ambiguity or confusion, these goals should be as specific as possible.
  • Measurable – Evaluation is essential. You need to be able to measure your advancement towards your goal through metrics or other methods. How will you measure your progress? What metrics or evidence will you track in order to assess your efforts? 
  • Achievable – Make sure you aren’t setting goals outside of your reach. While these goals should be challenging, it’s important to remain within the realm of attainability.
  • Relevant – Your goals should be tied to the broader goals of your organization and what your department is trying to achieve. 
  • Time-Based – Setting a timeline will help you manage your time and implement a sense of urgency into your efforts.

This strategy is effective in that it sets up clear and measurable targets, increasing your chances of knocking a project or metric out of the park. 

Your goals should be challenging enough so that you won’t be able to hit one every game and it’s by no means comparable to winning your organization’s world series, but it’s a small win helping to mark your development as a player. 

Around the Bases

Overall, this post demonstrates the many tactics and resources available at anyone’s disposal to immediately up your data game. Finding the BI all-star in you ultimately comes down to how you’re investing in yourself. Improvement is a slow and steady process, make the most of the knowledge around you and experiment with what interests you. Implement these tips and strategies to start your journey to the hall of fame!

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Artificial Intelligence Big Data Business Intelligence Data Analytics

Press Release: Inzata Analytics: Expanding its Reach to the DoD with Nobletech Solutions

Inzata Analytics, a data analytics software company, has announced its partnership with Nobletech Solutions Inc., a provider of technology and engineering services. This partnership will address the DoD’s full spectrum of data challenges ranging from prognostic & predictive maintenance (PPMx), logistics, and sustainment to intelligence, human resources, and fiscal management without the complexity and delays that existing DoD data analytics tools provide.

Inzata provides the software to bring data analytics to end-users within hours and days at all levels within DoD organizations, regardless of user-level experience. Nobletech provides the proper secure network, DoD experience, and contract vehicles that will put Inzata’s AI and data analytics solutions into the hands of those front-line users when the data is needed to make those critical decisions.

Inzata’s ability to assemble large disparate data sources without coding enables data analysis to occur in significantly shorter times than any of its competitors. The current political climate cannot afford to wait months and years for analysts and data architects to develop products for decision makers while Inzata’s AI/ML and no-code solution can have it done in days and weeks, vs. months and years that the competition requires.

Nobletech Solutions will bring Inzata into the DoD by leveraging their GSA Schedule while pursuing a DoD Enterprise Software Agreement (ESA). This will be done by demonstrating to the DoD and its industry partners the power of Inzata’s Artificial Intelligence/Machine Learning (AI/ML) capability to perform rapid assembly and analysis of data and visual dashboards through its unique no-code solutions.

It is certain that Nobletech Solutions with the ability to host on the DoD secret cloud, without the required add-ins and extras required by those other data analytics platforms, will surely be a significant mission enabler to special operations and other organizations within the DoD and OGA.

Nobletech Sr. Analyst: “Providing an edge to the DoD to quickly assemble large data with the help of AI/ML will certainly be a game changer. We envision Inzata as the tool to provide that big picture view of people, equipment, parts, location, training, intelligence, risk, environmental conditions, and other data needed by the most senior level commanders down to the small unit leader.”

Nobletech CEO: “We are excited to have this partnership and look forward to being a key player in meeting the needs of DoD’s data analytics pertaining to human resource, material, training/qualification, prognostic maintenance, and intelligence.”

For more information, please contact the following:
Jim Scala at jscala@nobletechsolutions.com
Luke Whittington at lwhittington@nobletechsolutions.com
You can also learn more by visiting https://www.nobletechsolutions.com/data-analytics

This press release was originally featured on PRWeb, find the press release here: http://www.prweb.com/releases/inzata_analytics_expanding_its_reach_to_the_dod_with_nobletech_solutions

Categories
Big Data Data Analytics Data Preparation

The Beginner’s Guide to Data Streaming

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What is Data Streaming?

Data streaming is when small bits of data are sent consistently, typically through multiple channels. Over time, the amount of data sent often amounts to terabytes and would be too overwhelming for manual evaluation. While everything can be digitally sent in real-time, it’s up to the software using it to filter what’s displayed. 

Data streaming is often utilized as an alternative to a periodic, batch data dump approach. Instead of grabbing data at set intervals, streamed data is received nearly as soon as it’s generated. Although the buzzword is often associated with watching videos online, that is only one of many possible implementations of the technology.

How is Data Streaming Used?

Keep in mind that any form of data may be streamed. This makes the possibilities involving data streaming effectively limitless. It’s proven to be a game-changer for Business Analytics systems and more. From agriculture to the fin-tech sector to gaming, it’s used all over the web.

One common industry application of data streaming is in the transportation and logistics field. Using this technology, managers can see live supply chain statistics. In combination with artificial intelligence, potential roadblocks can be detected after analysis of streamed data and alternative approaches can be taken so deadlines are always met.

Data streaming doesn’t only benefit employees working in the field. Using Business Analytics tools, administrators, and executives can easily see real-time data or analyze data from specific time periods. 

Why Should We Use Data Streaming?

Data silos and disparate data sources have plagued the industry for countless years. Data streaming allows real-time, relevant information to be displayed to those who need access to it the most. Rather than keeping an excessive amount of data tucked away on a server rarely accessed, this technology puts decision-driving information at the forefront.

Previously, this type of real-time view of business processes was seen as impossible. Now that it’s possible to have an internet connection almost everywhere, cloud computing makes live data streaming affordable, and Business Analytics tools are ready to implement data streaming, there’s no reason it would be inaccessible.  

While it may be tempting to stick to older ways of processing data, companies who don’t adapt to this new standard will likely find it more difficult to remain competitive over the years. Companies that do incorporate the technology will likely see their operations become more streamlined and find it much easier to analyze and adjust formerly inefficient processes.

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