Category: Big Data

Augmented Analytics: The Missing Piece of Business Intelligence

Post author By Scottie Todd
Post date December 20, 2022

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

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.
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.
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.

Big Data Data Analytics Data Quality

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

Post author By Scottie Todd
Post date November 13, 2022

Cloud storage banner background, remixed from public domain by Nasa

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.

Big Data Business Intelligence Data Analytics

Why You Need to Modernize Your Data Real Estate

Post author By Scottie Todd
Post date December 20, 2021

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.

Big Data

Why Everyone Hates Spreadsheets

Post author By Sagar B
Post date November 8, 2021

Spreadsheet Marketing Budget Report File Concept

It’s Time to Part Ways With Excel Spreadsheets for Data Analysis

Excel is excellent for some things, like performing quick calculations or keeping track of your personal spending. Heck, it’s even great for startup e-commerce shops with minimal inventory or sales. But for other tasks and bigger businesses, Excel spreadsheets can create more problems than solutions.

So, in an effort to hustle the world toward better IT solutions, we’re breaking down why everyone should be moving away from spreadsheets for data analysis work.

What Are the Pitfalls of Using Spreadsheets for Data?

Why don’t spreadsheets cut it anymore? There are a number of practical reasons for businesses and organizations to shy away from Excel. Some are simple functionality issues while others have only been recently discovered under specific work environments.

Overall, there are four main reasons: data inaccuracy, real-time update constraints, capacity breaks, and limited analytical parameters.

Data Inaccuracy

Spreadsheet accuracy is dependent on human accuracy — and that’s a recipe for disaster because it’s dangerously easy to mess up a field. Common mistakes include:

Mechanical Errors: Replacing formula fields with static numbers, keying in typos, and transferring mishaps rank among the most common mechanical spreadsheet errors. More than just simple mistakes, a single flub in one field can compromise the integrity of an entire workbook.
Logic Errors: Logic errors stem from bad formulas. Due to the relational nature of spreadsheets, a flawed foundational calculation has the power to compromise a whole document.
Errors of Omission: Due to workplace pipeline breakdowns, data can simply be left off spreadsheets. Unless there are validation checks built into your system, discovering such errors of omission may be impossible.

Lack of Real-Time Updates

Another problem with spreadsheets is their static nature. While several people can access a single document, things become easily jumbled when two or more people try to change it simultaneously. In many instances, the last person to enter data is not the person with the right figures.

Mistakes like this have a ripple effect, and it can be weeks before the problem is accurately identified — if it’s ever caught at all!

Capacity Breaks

In 2020, over 15,000 COVID-19 cases went unreported in the United Kingdom — all because of an Excel spreadsheet.

What happened?

Well, Public Health England (PHE) used Excel to collate data from hospitals and medical clinics across the country. But what the agency failed to realize is that the Excel version running on its network had a 65,536-row limit. To shorten a long story, the number of cases exceeded the cap, and the oversight triggered an administrative nightmare.

Excel was forged in the crucible of early tech — before the days of big data — and it still hews to the limited footprint of that time.

One-Dimensional Analysis

Spreadsheets were made for arithmetic and a bit of elementary calculus. But today’s data analysis procedures use more complex, multi-faceted approaches. Plus, you cannot measure progress or see status updates on spreadsheets, and the physical view is confined to a row-column setup, which forces constant back and forth scrolling.

These one-dimensional limitations are time wasters that ultimately eat into your bottom line.

What Are Some Spreadsheet Alternatives?

These days, there are thousands of superior programs that have muscled in on Excel’s market share. The trick is finding the ones that work best for your business and market niche. Partnering with an AI-powered data analysis platform is usually the way to go, as they can produce real-time insights and develop robust solutions tailored to your needs.

It’s time to move on from inefficient spreadsheets. Using one to coordinate game night is great, but demonstrably better options are available for data analysis and business projects.

Big Data Business Intelligence

Making Sense of IoT Sensors, MQTT, and Streaming Data

Post author By Scottie Todd
Post date October 29, 2021

Cloud network system with digital technology, remixed from public domain by Nasa

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.

Conclusion

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.

Big Data Data Preparation

6 Core Principles Behind Data Wrangling

Post author By Scottie Todd
Post date October 11, 2021

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.

Discovery

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.

Structuring

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.

Cleaning

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.

Enriching

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.

Validating

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.

Publishing

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.

Back to blog homepage

Big Data Data Preparation

Indexing & Metadata: How to Deal with Video and Unstructured Data

Post author By Scottie Todd
Post date September 2, 2021

Solutions for Unstructured Data That Includes Video

If you’ve landed on this page, there’s a good chance you’re sitting on a mountain of unstructured data, specifically an abundance of video files. Your goal is to parse, organize, and distribute the information in such a way that makes it the most useful to the greatest number of people in your organization. But unstructured data can be as unruly and difficult to manage as a bag of snakes. So the question becomes: How can you tame it?

What’s the Problem With Unstructured Video Data?

So what’s the problem with unstructured data? As is the case with a tangle of wires, the hurdle with unstructured data is that it’s difficult to classify, manage, organize, and distribute. And ultimately, what’s the use of collecting loads of information if you can’t do anything with it? When videos are tossed into the mix, things become even more complicated because they’re not easily searchable in text-based database systems.

But before you can develop a plan to sort out the mess, you must define the data goals. Ask yourself a few key questions such as:

Who needs access to the information?
For what are they using it?
How does the intended data use support the company’s overarching goals?

Unstructured Video Data: Indexing

Indexing is a database optimization technique, which preprocesses information and allows for faster querying. It’s an advanced database administration skill that requires the programmer to account for many options, like missing values and form errors.

When videos are in the data mix, indexing is even more complicated. However, by setting up a simple save-and-catalog function, it’s manageable. So how do you do it?

First, save the video file on the network. Make sure it’s somewhere accessible to the people who will need it. Also, ensure that people can’t change file names easily. If they do, it can “break” the database. Then, catalog each A/V file by including GUID keys that point to where they sit on the network.

If greater specificity is needed, make a record — and corresponding line item — for each video frame. Yes, it’s time and labor-intensive, but the effort is often worth it to mine intelligent data.

Unstructured Video Data: Metadata

After creating the index, the next step is gathering, storing, and linking the appropriate metadata, which may include the date, length format, EXIF info, and source. Cataloging the metadata is vital because it provides a searchable and filterable field for the video file line item.

Sometimes, you may want to write some metadata to the file name as a backup. You can achieve this by structuring the file names like [DATE]_[GUID].mp4. By doing so, team members can quickly determine to which record the line item is tied.

Let’s Discuss Your Unstructured Data Needs

Outsourcing database logistics to a third party can be the ideal solution because it frees up internal resources for profit-generating activities. Plus, partnering with database experts can decrease costs associated with employment.

Inzata Analytics’s team has considerable experience empowering businesses, non-profits, schools, and government entities to maintain their unstructured databases. Reach out today. Let’s start the conversation.

Back to blog homepage

Big Data Data Quality

How to Master Modern Data Governance

Post author By Scottie Todd
Post date August 30, 2021

Data governance, though often overlooked, offers businesses a host of benefits. Keeping data up-to-date, accurate and complete often poses challenges for many business leaders. Thankfully, with the proper knowledge, tools, and patience, data and analytics leaders can build a team and utilize various available support systems to overcome these barriers and master data governance within their organization.

What Is Data Governance and Why Is It Important?

At its core, data governance focuses on the following:

Keeping data accurate and updated as needed
Controlling how, where, when, and by whom data is used within a company
Managing data integrity
Detecting, deleting, and merging duplicate data files within the file system
Ensuring all data reports are correct for compliance and regulatory purposes

Therefore, it’s obvious why data governance is an essential part of most workplace operations. Many businesses heavily rely on storing and retrieving information for future use. For this reason, duplicate information, customer profiles, and disorganized data tracking can lead to significant issues. Without correctly managed data, numerous departments can struggle to perform their jobs correctly. These issues can result in a loss of productivity, increased costs, and even impact long-term customer retention.

Finally, it’s also important to note that storing data correctly and carefully monitoring how, when, where, and who uses stored data is also essential. Several regulatory agencies require companies to report on how they store and use consumer data. Others monitor data use and enforce transparency regarding certain types of information. Though, monitoring and governing data then become fundamentals to remaining in compliance with these regulatory agencies.

How Can Companies Master Data Governance?

Mastering data governance is no easy task, but it is critical to most businesses, no matter the size. Thankfully, through the help of available tools and the assistance of data and analytics professionals, data governance becomes a manageable task. Here are a few key strategies organizations use to organize, analyze and maintain data integrity successfully.

Determine the needs of the organization and align them with data governance solutions. This step serves as the stepping stone for all data governance plans. Many companies find themselves frustrated with the way data is managed across the departments, as governance practices are often mistakenly data-based rather than business-based. Determining how employees use data, how often it is retrieved and accessed, and who can make permanent changes to records allows organizations to manage their information effectively.

Determine key performance indicators. During this phase, data and analytics leaders should also consider outlining and implementing key performance indicators, or KPIs, for managing their data. KPIs allow businesses to use measurable metrics to determine the overall success of their data governance practices. Over time, organizations can use these KPIs to make adjustments to their data governance plans. By measuring KPIs, data governance becomes a practice of using data to align with business needs and moves away from the traditional expectations of data storage.

Develop risk management and security measures for stored data. Finally, many governing agencies require companies who store data to remain accountable and transparent regarding data security. Therefore, modern data governance plans include a variety of layers of protection. Companies should consider the following when developing their risk management programs:

Who is interacting with private information regularly, and have they received the required compliance training?
When do individuals need to access stored data, and when can they change it?
What measures are in place to protect outsiders from accessing private information?

This step often involves working alongside your cyber security and legal teams to determine the appropriate action steps for data security.

Who Should Understand Data Governance?

Ultimately, any individual within an organization who may access, store or update data used by a company should receive training on data governance. Once you’ve developed a high-quality governance plan, ensuring each individual within your company who interacts with stored data understands the organization’s data governance practices is essential.

Furthermore, ensuring data integrity and accuracy may involve revisiting certain practices, changing methodologies, updating information, and providing additional company-wide training. Therefore, mastering modern data governance requires organization-wide cooperation and consistent monitoring to keep data consistent and error-free.

Back to blog homepage

Big Data Data Analytics

7 Data Analytics Mistakes Digital Marketers Make

Post author By Scottie Todd
Post date August 16, 2021

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.

Back to blog homepage

Big Data Data Analytics Data Modeling

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

Post author By Scottie Todd
Post date August 5, 2021

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.