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Category: Data Analytics

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Business Intelligence Data Analytics Data Visualization

How to Avoid the 5 Most Common Data Visualization Mistakes

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Why Do Data Visualization Mistakes Matter?

Although data visualization has been in place since 1780, when the first bar chart was produced by a toy company in Scotland, the practice is still imperfect. Both intentional, misleading data visualization “mistakes” as well as honest mistakes made during output are more common in the business world than one might think.

Intentional “Errors”

When an organization wants to get a point across without providing much evidence, “statistical manipulation” is commonplace. Though a dishonest practice, it’s still widely seen in business today. Typically, organizations will leave out the scale on a bar graph or pie chart. Then, they will intentionally emphasize disparities or relationships in the data, with no actual scale to which viewers can compare each bar.

Virtually any data set can be made to look off-target using this method. While experienced analysts would be able to question or see right past this type of reporting, individuals unfamiliar with the data may not. As a side effect, this manipulation and bias could lead to a loss of credibility or potential revenue.

Unintentional Errors

The “weakest link” in the chain of statistical reporting is often the human generating the report. Even if there’s no reason for the person making the report to be misleading, their reports could unintentionally appear this way. Most often due to a lack of experience or context on the matter, these mistakes look deceiving and can result in a loss of integrity.

Who is Responsible for These Mistakes?

Most organizations have several layers of employees. While a report may be generated by an individual analyst, the responsibility for its contents is typically on the department that ends up releasing it. 

It can be hard to take a step back and think objectively when you’re the one working so closely with the data. This is why it’s critical to get multiple perspectives on the veracity of your reports before releasing them. Alternatively, you may choose to train an internal department that reviews every data set before it’s released to the public or another company.

What Are the Five Most Common Mistakes?

While there is an abundance of potential mistakes that could occur during the creation of a data set, some are more common than others. Here are the five issues we see the most often when it comes to data visualizations. They are important to avoid as all of these can be harmful to a company’s reputation and credibility overall.

1. Unlabeled X-Axis Start

A common technique in intentional data distortion, this is an abuse of the common conclusion that readers would draw from your chart. Unless otherwise marked, readers assume that your X-axes start at 0. Starting them at a higher number to emphasize smaller disparities is beyond merely “tweaking” a chart.

2. An Inverted Y-Axis

Elementary school-level math taught most of us that our X-axis and Y-axis should start at zero and go up from there. If an analyst wants to convey a message that’s the opposite of the results, flipping an axis is a great way to do that. However, this method rarely pays off due to the irregular visualization. Experienced viewers will undoubtedly detect this. 

3. Scale Truncation

We all expect bar charts to be linear in nature. However, if someone generating the chart wants a number to appear lower than it actually is, truncating it is the way to go. This is when you might see a small squiggle in a bar chart that randomly cuts out a large number. Ostensibly, the reason is usually to “keep it all on one page.” However, simply changing the scale rather than truncating arbitrary columns is how to keep it honest.

4. Cherry-Picking Scales

This is when a chart has data in arbitrary units. These are typically (but not always) intentionally engineered to make two scales either as close to each other as possible or as far away from each other as possible. It’s important to use the same units wherever possible. If it’s not possible, this must be clearly distinguished.

5. Including Too Much Data

Not always done intentionally but confusing nonetheless, this is when a chart has far too much data for the average reader to interpret. Charts should be kept as simple as possible. This will allow viewers to quickly and easily understand the information presented. 

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Big Data Business Intelligence Data Analytics

Augmented Analytics: The Missing Piece of Business Intelligence

Augmented Analytics The Missing Piece Of Business Intelligence

Can you believe it? We’ve made it to 2023. And truth be told, it’s a pretty sci-fi time to live. People carry around pocket computers, celebrities are “beamed” into performances, and increasing numbers of people consider phone calls quaint.

The same rate of technological progress has also consumed the business world. Like phone calls, companies that still use analog methods are throwbacks. These days, big data and augmented analytics are fueling the market, and businesses that refuse to adapt may find themselves at the back of the pack.

What Is Augmented Analytics?

Augmented analytics is shorthand for “using advanced technology to squeeze more out of business analysis efforts.” Artificial intelligence and machine learning are now commonplace, and they’ve transformed the data analysis landscape. Not only can we glean valuable insights about product pipelines, back-office operations, and customer interactions, but automation possibilities have also improved significantly.

Augmented analytics programs touch every point of the data lifecycle, from preparation to implementation.

How Can Augmented Analytics Help Your Company?

Augmented analytics isn’t just the buzzword of the quarter. Instead, think of it as the next “Internet.”

Back in the day, many companies didn’t see the value of the Internet or websites and cynically dismissed both as fads. When it became evident that the “World Wide Web” was here to stay, businesses that didn’t establish a digital foothold were caught on the backfoot — and catching up was prohibitively expensive in many cases.

In a way, we’re at a similar inflection point regarding big data. Businesses that got in early are reaping the financial benefits and winning market share. Companies that wait too long may find themselves hopelessly behind the eight ball.

How do big data and augmented analytics give organizations an edge? They uncover hidden operational pitfalls and possibilities, deliver value faster, and increase data intelligence.

Uncovers Hidden Pitfalls and Possibilities

Augmented analytics provides a clearer, more dynamic view of a company’s operations and sales. As such, it’s easier to spot and leverage trends.

Delivers Value Faster

Analog back-office operations consume a lot of resources and time. After all, manually entering every record, one by one, will take significantly more hours than a semi-automated system that can cycle through data by the microsecond.

Increased Data Intelligence

Computers can do amazing things. Heck, commonplace systems are smarter than we are in many regards. Marketing models can pinpoint potential customers and clients, increasing conversion rates and, ultimately, your bottom line.

Augmented Analytics Best Practices

It’s important not to conflate augmented analytics with full automation. Though the latter informs and supports the former, augmented analytics systems require people power. So when transferring to an augmented analytics system, hew to these three best practices

  1. Start Small: Don’t try to implement a new system all at once. Start with a small project that best serves your key performance indicators.
  2. Collaborate: Lack of transparency can hamstring an AI implementation. Make a seat at the table for every department that will use and benefit from the data. The best systems are ones that include input from across the board.
  3. Educate Employees About the Advantages of a Data-Driven Culture: The more employees understand the power of analytics, the more enthusiastic they’ll be about the process. After all, if the company prospers, that’s great for them, too!

How Is Augmented Analytics Transforming Business Intelligence and Data Analytics?

Augmented analytics is the third stage of the business intelligence metamorphosis.

  • First Stage Is Traditional Business Intelligence: The first iteration of business intelligence is known as “the traditional stage.” Under these setups, data engineers mold static dashboards, reports take days to prepare, and cross-departmental collaborations are rare. While most traditional processes feature elementary computer modeling, data entry and manipulation are 100% manual.
  • Second Stage Is Self-Service Business Intelligence: Self-service business intelligence options grew up alongside web 2.0. Hardware and software updates simplify the informational pipeline and provide better modeling, reporting, and data analysis. Automation is more prevalent for routine tasks under second-stage systems. However, the digital apparatus is limited to drag-and-drop options that may require advanced knowledge.
  • Third Stage Is Augmented Analytics: Augmented analytics programs leverage artificial intelligence to streamline the data prep stage, allowing for real-time analysis. Moreover, since the systems are highly intuitive, they’re accessible to more employees. To state it another way: employees no longer need to be data scientists to be part of — and benefit from — a company’s analytics pipeline.

If you’re contemplating an augmented analytics upgrade, it’s wise to consult with industry-leading platforms, like Inzata Analytics.

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Big Data Data Analytics Data Quality

Optimize big data solution: warehouses, lakes, lakehouses compared.

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Which Big Data Solution Is Best for You? Comparing Warehouses, Lakes, and Lakehouses

Big data makes the world go round. Well, maybe that’s an exaggeration — but not by much. Targeted promotions, behavioral marketing, and back-office analytics are vital sectors fueling the digital economy. To state it plainly: companies that leverage informational intelligence significantly boost their sales.

But making the most of available data options requires tailoring a platform that serves your company’s goals, protocols, and budget. Currently, three digital storage options dominate the market: data warehouses, data lakes, and data lakehouses. How do you know which one is right for you? Let’s unpack the pros and cons of each.

Data Warehouse

Data warehouses feature a single repository from which all querying tasks are completed. Most warehouses store both current and historical data, allowing for a greater breadth of reporting and analytics. Incoming items may originate from several sources, including transactional data, sales, and user-provided information, but everything lands in a central depot. Data warehouses typically use relational tables to build profiles and analysis metrics.

Note, however, that data warehouses only accommodate structured data. That doesn’t mean unstructured data is useless in a warehouse environment. But incorporating it requires a cleaning and conversion process.

Pros and Cons of Data Warehouses

Pros

  • Data Standardization: Since data warehouses feature a single repository, they allow for a high level of company-wide data standardization. This translates into increased accuracy and integrity.
  • Decision-Making Advantages: Because of the framework’s superior reporting and analytics capabilities, data warehouses naturally support better decision-making.

Cons

  • Cost: Data warehouses are powerful tools, but in-house systems are costly. According to Cooldata, a one-terabyte warehouse that handles about 100,000 queries per month can run a company nearly $500,000 for the initial implementation, in addition to a sizable annual sum for necessary updates. However, new AI-driven platforms allow companies of any size to design and develop their data warehouse in a matter of days, plus at a fraction of the price. 
  • Data Type Rigidity: Data warehouses are great for structured data but less so for unstructured items, like log analytics, streaming, and social media bits. Resultantly, it’s not ideal for companies with machine learning goals and aspirations.

Data Lake

Data lakes are flexible storage repositories that can handle structured and unstructured data in raw formats. Most systems use the ELT method: extract, load, and then transform. So, unlike data warehouses, you don’t need to clean informational items before routing them to data lakes because the schema is undefined upon capture.

At first, data lakes may sound like the perfect solution. However, they’re not always a wise choice — data lakes get very messy, very quickly. Ensuring the integrity and effectiveness of in-house systems takes several full-time workers who do nothing else but babysit the integrity of the lake.

Pros and Cons of Data Lakes

Pros

  • Ease and Cost of Implementation: Data lakes are much easier to set up than data warehouses. As such, they’re also considerably less expensive.
  • Flexibility: Data lakes allow for more data-type and -form flexibility. Moreover, they’re equipped to handle machine learning and predictive analytics tasks.

Cons

  • Organizational Hurdles: Keeping a data lake organized is like trying to keep a kid calm on Christmas morning: near impossible! If your business model requires precision data readings, data lakes probably aren’t the best option.
  • Hidden Costs: Staffing an in-house data lake pipeline can get costly fast. Data lakes can be exceptionally useful, but they require strict supervision. Without it, lakes devolve into junkyards.
  • Data Redundancy: Data lakes are prone to duplicate entries because of their decentralized nature.

Data Lakehouse

As you may have already guessed from the portmanteau, data lakehouses combine the features of data warehouses and lakes. Like the former, lakehouses operate from a single repository. Like the latter, they can handle structured, semi-structured, and unstructured data, allowing for predictive analytics and machine learning.

Pros and Cons of Data Lakehouses

Pros

  • Cost-Effective: Since data lakehouses use low-cost, object-storage methods, they’re typically less expensive than data warehouses. Additionally, since they operate off a single repository, it takes less manpower to keep lakehouses organized and functional.
  • Workload Variety: Since lakehouses use open-data formats and come with machine learning libraries like Python/R, it’s easier for data engineers to access and utilize the data.
  • Improved Security: Compared to data lakes, data lakehouses are much easier to keep secure.

Cons

  • Potential Vulnerabilities: As with all new technologies, hiccups sometimes arise after implementing a data lakehouse. Plus, bugs may still lurk in the code’s dark corners. Therefore, budgeting for mishaps is wise.
  • Potential Personnel Problems: Since data lakehouses are the new kid on the big data block, it may be more difficult to find in-house employees with the knowledge and know-how to keep the pipeline performing.

Big data collection, storage, and reporting options abound. The key is finding the right one for your business model and needs.

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Business Intelligence Data Analytics

3 Strategies to Accelerate Digital Transformation

Three Strategies to Accelerate Digital Transformations

We’re well into the Digital Age, but some businesses have yet to harness computing power. Sure, they may be drowning in company devices and have accounts with the “right” platforms, but are they properly leveraging the tools they have? Surprisingly, in many instances, the answer is “no.”

Making a true digital transformation requires long-term strategic planning and precise implementation.

What Is Digital Transformation?

Digital transformation is the process of upgrading your business operations to fully leverage the power of computing and business intelligence systems. The metamorphosis from analog to digital involves more than just stocking up on the latest and greatest devices. Instead, digital transformations are complete procedural overhauls informed by a 360-degree analysis of your market and company.

What Are the Fundamental Tiers of a Digital Transformation?

Computer engineers typically divide digital transformation projects into four tiers:

  • Operational Efficiencies: How can we improve our production or service pipeline with enhanced digital integration?
  • Advanced Operational Efficiencies: How can we collect, analyze, and leverage information gleaned from customer and client interactions with our products and services?
  • Data-Driven Services Rooted in Value Chains: How can we leverage big data to create new market-making, customer-oriented services?
  • Data-Driven Services Rooted in Digital Enhancements: How can we collect market-making data, via the products and services we create, by digitally enhancing our offerings?

Why Is it Important to Invest in the Right Software and Tools?

One of the biggest mistakes companies make is not investing in the right tools and software for their operation. What’s “new” isn’t always ideal, and focusing on the needs of your business should be the top priority. Before committing to a digital transformation, ask yourself questions like:

  • How much money can we safely commit to the project without overextending the business?
  • What sectors of our business are working well and which need optimizing?
  • What are our team member’s computer competencies? What is the learning curve?

How Can You Accelerate Your Company’s Digital Transformation?

Analyze Operations: The first step in a digital transformation is analysis. Whether you conduct an in-house review or hire a skilled third party that helps companies navigate wall-to-wall computational upgrades, it’s essential to start with an accurate assessment of the business’s operations.

Analyze Customers: After you take stock of back-office operations, it’s time to peel back the layers on your customers. Invest in a thorough examination of how the people who use your services and products interact with them.

Match Competencies and Leverage Technologies: Once you have a 360-degree view of your operations and customer interaction, it’s time to pick your technologies. Finding solutions that fit your team’s budget and skills will help ensure the best possible outcome.

We are well into the digital age, and waiting to embark on a digital transformation is no longer an option. Tackle one step at a time, enlist experts, and take the plunge.

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Big Data Business Intelligence Data Analytics

Why You Need to Modernize Your Data Real Estate

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Business data analysis

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.

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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.

Conclusion

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.

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Business Intelligence Data Analytics

8 Questions You Need to Ask Before Building a Data Warehouse

Does your business need a data warehouse? On one hand, they accommodate complex modeling, improve workflows, and ultimately increase profits. On the other hand, data warehouses can be resource-intensive and expensive, rendering them impractical in certain situations. 

1. How Long Have You Been Collecting Data?

Are you sitting on a pile of customer data? If not, you might not be ready for a warehouse just yet. Startups may find that capital is better spent securing personnel, building infrastructure, branding, and marketing. However, if you’re sitting on mounds of information ripe for analyzing, investing in data warehousing could be the game-changer to your bottom line.

2. Do You Have a Lot of Reporting Systems?

Is your current reporting environment a patchwork of different systems held together by a fraying thread? If so, why not streamline and house everything under a single data warehousing umbrella. Not only will it improve workflow, but you’ll be better able to investigate historical data and compare it to recent trends.

3. Do You Have Custom Reporting Systems?

Custom reporting is the backbone of many businesses. If your company relies on specialized reports created in the corporate dark ages, it’s probably time to update, and moving to a data warehousing model may be ideal. Querying becomes infinitely easier with a centralized system as opposed to a siloed setup.

4. In What Formats Is Your Data Stored?

Data comes in a variety of forms. If over the years, you’ve switched management systems, legacy data may be in formats that no longer work with your current setup. By investing in a data warehousing system, you’ll be able to create a digital ecosystem that accommodates multiple formats, which are normalized at the extract, transform, and load — aka ETL — stage.

5. Are Your Modeling Efforts Complex?

The more complex data and reporting needs are, the more helpful a data warehouse can be. Plus, having a streamlined system may illuminate new metrics that can be studied and leveraged.

6. Are You Frustrated by Reporting Performance Issues?

When reporting against operational systems, data can become volatile. Information sets can morph into forms, like a substance changing from ice to gas. When it happens, your reporting can become filled with errors. However, data warehousing mechanisms, which are typically optimized for read-access only, often eliminate the querying and processing kinks, resulting in fewer output headaches.

7. Do You Need to Perform Multi-Year Data Transformations?

Businesses with multi-generational data frequently benefit from a data warehouse. Insightful and profitable realizations can be mined with a powerful querying system.

8. What Resources Are at Your Disposal?

Though often worth it in the long run, developing a data warehousing system can be a costly and extensive process, especially if you attempt to build one without the help of advanced tools. The return on investment isn’t always immediately evident. So if the money isn’t readily available, it may not be time. After all, a poorly executed data warehouse can waste time and productivity. Wait till you have the resources to get it done correctly or enlist the help of AI-powered tools.

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Big Data Data Analytics

7 Data Analytics Mistakes Digital Marketers Make

It’s no question that a marketing campaign can gather tremendous amounts of data, however, if the data is not correctly interpreted the value may only be a fraction of its potential. According to a recent Gartner survey, marketers sensed their companies did not fully understand how to effectively leverage data analytics. Let’s dive into the common challenges and mistakes marketers face when it comes to their data analytics.

The 7 Most Commons Mistakes Digital Marketers Make

1. Confusing Data Metrics and Visualizations

A clear understanding of what metrics actually are rather than what they are “perceived’ to be is essential to any marketing campaign. Marketers should have a clear understanding of what the metric means, not purely what is presented in a visualization. For example, unless there is a precise understanding of what “views” represent as opposed to “visits”, analytical data can be easily misconstrued. 

Depending on training and expertise, some marketers may not necessarily be data experts. This highlights the need for strong background information when it comes to dashboards and data visualizations. Without proper context, it can be overwhelming when determining the correct course of action. It is imperative to not choose a visualization based upon the flashiest dashboard design but to understand the data behind the visual, this will ensure proper decision making and evaluation.

2. Relying on a Single Data Set

Data analytics requires collecting data and often there may be more than one tracking source for the data collection. Oftentimes different data tracking mechanisms may generate various data metrics from the same data collection. It is vital to work with numerous tracking sources for increased visibility across target audiences and campaign performance, whether they be internal or external. Aim to collect both qualitative and quantitative data for the most accurate and informative visibility.

3. Incorporating Data Too Late into the Creative Process

The marketer’s creative process should be the end result of the primary marketing objective. Though, the creative process can be more powerful when incorporating analytical data elements. 

Being able to drill down into your audience’s preferences and demographics is a winning process in creative production. Some key takeaways from incorporating data early in the creative process are:

 1. The earlier you can incorporate data analytics in the creative process, the better.

 2. Utilize the collected information to clearly define your key audience.

 3. Leverage data to create a road map of how to reach your targeted audience.

4. Concentrating Heavily on Vanity Metrics

A marketer understands many elements go into creating captivating content and copy. Though, the positive feedback for a video or campaign generating thousands of comments, likes, followers, or other vanity metrics may lead to a false sense of success. 

The key question and focus should continue to be towards quantifiable conversions and investment in the customer lifetime value. Access if the marketing efforts ultimately lead to loyal customers evangelizing the brand. The focus should remain on generating leads, then conversions, and sequentially creating loyal customers.

5. Not Asking Questions

Data analytics is very efficient in creating a comprehensive set of data, and studying a report or spreadsheet to form a clear picture can be daunting. The trick is to have an explicit focus on your end goals and intentions, asking questions is key to narrowing down the data points required to formulate a winning conclusion.

For example, when studying the data, the question may not be to see “how the website is performing” but rather asking “how much has our social traffic increased?” When questions are asked about specific data points, the answers should guide you to more productive conclusions.

6. Ignoring the Importance of Data Culture

Buy-in across the organization is critical to any successful analytics strategy. Commonly, few on the team have a clear understanding of the importance of being data-driven. High-level goals that data analytics will be a cornerstone for the marketing process should be known and understood across all levels of the organization. Try implementing an objective to embrace data analysis by defining obtainable goals and gradually increase awareness through training and workshops.

7. Failure to Create Actionable Insights

Actionable insights require looking beyond the surface level of standard metrics and KPIs. While not all conclusions may be useful, particularly without fully comprehending what they indicate, not diving deeper into analytical conclusions may lead to lost opportunities. Make sure to analyze the metrics in-depth for patterns and unique insights. By diving deeper into insights and taking an exploratory approach, successful strategies may begin to form. 

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Big Data Data Analytics Data Modeling

Data Lake vs. Data Warehouse: What’s the Difference?

What is the Difference Between a Data Lake and Data Warehouse?

To begin, the two offer similar functions for business reporting and analysis. But they have different use cases depending on the needs of your organization. 

A data lake acts as a pool, storing massive amounts of data kept in a raw state. This can be used to store structured, semi-structured, and unstructured data from a variety of sources such as IoT devices, mobile apps, social media channels, and website activity. 

A data warehouse, on the other hand, is more structured unifying data from multiple sources that have already been cleansed through an ETL process prior to entry. Data warehouses pull data from sources such as transactional systems, line of business apps, and other operational databases. Another principal difference between the two is how each makes use of schema. A data warehouse utilizes a schema-on-write, while a data lake makes use of schema-on-read. 

When it comes to users, a data warehouse is typically used by a broader range of roles such as business analysts using curated data, along with data scientists and developers who focus on driving insights from the raw data to obtain more customized results.

Who Benefits From Each Type? 

Depending on your organization, you can actually benefit from both types of data storage solutions. A combination of one or both can benefit your business depending on your data stack and requirements for data analysis and reporting. 

Historically, data lakes are used with companies that have a dedicated support team to create, customize, and maintain the data lake. The time and resources needed to create the data lake can be extensive, but there is also a wide selection of open source technologies available to expedite the process. If you need to handle large amounts of raw data as well as flexibility, this may be a good solution for you. 

If you need a solution that’s ready to go, a data warehouse platform provides you with a structured setup that can be a good option for analytics teams. Data warehouses typically cost more than data lakes, particularly if the warehouse needs to be designed and engineered from the ground up. Though AI-powered tools and platforms can drastically advance the building timeline and minimize expenses, some companies still take the in-house approach. Overall, data warehouses can be vital to companies that need a centralized location for data from disparate sources and accessible ad-hoc reporting.

Why Should You Use a Data Lake or Data Warehouse? 

Advanced tools make data warehouse design simple to set up and get started. These are typically offered as an integrated and managed data solution with pre-selected features and support. These can be a great option for a data analytics team due to their quick querying features and flexible access. If you need a solution that offers a robust support system for data-driven insights, a data warehouse may be right for you. 

If you prefer a quicker DIY method, a data lake might be a better solution. Data lakes can be customized at all levels such as the storage, metadata, and computing technologies based on the needs of your business. This can be helpful if your data team needs a customized solution, along with the support of data engineers to fine-tune and support it. 

What Should Be Considered When Selecting a Solution? 

At the end of the day, your business may need one or both of these solutions in order to gain high-level visibility across your operations. This holistic approach has led to the development of newer solutions that combine the vital features of both. The data lake house takes advantage of the more common data analytical tools along with added agents such as machine learning. 

Another factor to consider is the amount of support that your analytic teams currently have. A data lake typically needs a dedicated team of data engineers, which may not be possible in a smaller organization, but as time goes on, data lake solutions are becoming more user-friendly and require less support. 

Before selecting one of the two, take a look at who your core users will be. You should also consider the data goals of your company to understand the current and future analytics needs. What may work for one company may not work for yours, and by taking a closer look, you can find a data solution that best meets the needs of your business.

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How to Drive Growth & ROI with Marketing Analytics

How To Drive Growth & ROI With Marketing Analytics

What is Marketing Analytics?

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

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

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

1. Measure the effectiveness of marketing strategies and campaigns. 

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

Why Do Marketing Analytics Matter?

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

 Marketing analytics matter because: 

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

How Can Marketing Analytics Drive Growth?

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

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

  • Age
  • Gender
  • Geographic Location
  • Income Level

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

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

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

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

Which Marketing Analytics Deliver the Best ROI?

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

1. High-Quality Data

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

  • Current
  • Consistent 
  • Precise 
  • Accurate
  • Relevant 

2. Combine Past, Present, and Future Data

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

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