Categories
Big Data Business Intelligence Data Analytics

Why You Need to Modernize Your Data Platform

Effective use of data has become more important to modern businesses than many could have imagined a decade ago. As a piece on why every company needs a data strategy back in 2019 put it, data now “matters to any company” and is “one of our biggest business assets” in the modern environment. These are indisputable statements at this point, and they’re why every business hoping to succeed today needs to modernize its data platform (if it hasn’t already).

That said, even among those who like the idea of embracing data, many don’t quite understand what modernizing means in this sense. In this piece, we’ll look at why this needs to be done, who needs to do it, and what, ultimately, the process entails.

Why Modernize Data?

In very general terms, we addressed the why above: Effective data usage is indisputably one of the greatest assets available to businesses today. More specifically though, the role of data in business comes down to insight across various departments and operations. A robust data operation allows companies to understand needs and develop detailed processes for hiring; it enables marketing departments to make more targeted and fruitful efforts, and it helps management to recognize internal trends that drive or detract from productivity, and act accordingly. Modern data essentially streamlines business and makes it more efficient across the board.

We would also add that for smaller businesses, the why comes down to competition. The democratization of data in modern times is giving smaller companies the means to match larger competitors in certain efforts, and thus giving them a chance to keep pace.

Who Modernizes Data?

The answer to who brings about data modernization within a company will vary depending on the size and resources of the company at hand. For smaller businesses or those with particularly limited resources, it is possible to make this change internally. Much of the data modernization process comes down to using tech tools that can gather and catalog information in a largely automated fashion.

At the same time though, companies with more resources should consider that data analytics is a field on the rise, and one producing legions of young, educated people seeking work. Today, countless individuals are seeking an online master’s in data analytics specifically on the grounds that the business data analytics industry is in the midst of a projected 13.2% compound annual growth rate through 2022. Jobs in the field are on the rise, meaning this has become a significant market. This is all to say that it’s reasonable at this point for businesses seeking to modernize their data operations to hire trained professionals specifically for this work.

What Should Be Done?

This is perhaps the biggest question, and it depends largely on what a given business entails. For instance, for businesses that involve a focus on direct purchases from customers, data modernization should focus on how to glean more information at the point of sale, build customer profiles, and ultimately turn advertising into a targeted, data-driven effort. Businesses with large-scale logistics operations should direct data improvement efforts toward optimizing the supply chain, as Inzata has discussed before.

Across almost every business though, there should be fundamental efforts to collect and organize more information with respect to internal productivity, company finances, and marketing. These are areas in which there are always benefits to more sophisticated data, and they can form the foundation of a modernized effort that ultimately branches out into more specific needs. 

At that point, a business will be taking full advantage of these invaluable ideas and processes.  

Written by Althea Collins for Inzata Analytics

Categories
Big Data Data Preparation Data Quality

How to Solve Your Data Quality Problem

Why Does My Data Quality Matter?

One of the prime goals of most data scientists is to maintain the quality of data in their domains. Because business analytics tools rely on past data to make present decisions, it’s critical that this data is accurate. While it’s plenty easy to continually log information, you can risk creating data silos, large quantities of data that end up never really being utilized. 

Your data quality can directly impact whether and to what degree your company succeeds. Bad data can never be completely filtered, even with the best BI tools. The only way to base a future business decision on quality data is to only collect quality data in the first place. If you’re noticing that your company’s data could use a quality upgrade, it’s not too late!

What Are Some Common Mistakes Leading to Bad Data Quality?

By simply not engaging in a few practices, your company can drastically cut back on the volume of bad data you store. First, remember that you shouldn’t automatically trust the quality of data being generated by your current enterprise tool suite. This should be evaluated by professional data scientists to determine quality. Quite often, older tools generate more junk data than modern tools with better filtering technology.

Another common mistake is to allow different departments within your company to isolate their data away from the rest of the company. Of course, depending on the department and nature of your company, this could be a legal requirement. However, if not, you should ensure that there’s a free flow of data across business units. This can create an informal “checks and balances” system and help prevent those data silos from building or destroy existing ones.

How Can I Identify Bad Data?

Keeping in mind that, even with the best practices in place, it’s unrealistic to expect a total elimination of risk associated with bad data being collected. With the volume of enterprise tools in usage combined with even the most minor human error in data entry having the potential to create bad data, a small amount should be expected. That’s why it’s important to remain vigilant and regularly check for these items in your existing data and purge those entries if found:

  • Factually False Information – One of the more obvious examples of bad data is data that’s entirely false. Almost nothing could be worse to feed into your BI tools, making this the first category of bad data to remove if found.
  • Incomplete Data Entries – Underscoring the importance of mandating important database columns, incomplete data entries are commonly found in bad data. These are entries that cannot be fully interpreted without the information that’s missing being filled in.
  • Inconsistently Formatted Information – Fortunately, through the power of regular expressions, this type of bad data can often be solved fairly quickly by data scientists. A very common form of this is databases of telephone numbers. For example, even if all of the users are in the same country, different formats like (555) – 555-5555, 5555555555, 555-5555555, etc., are often present when any string is accepted as a value for the column.

What Can I Do Today About Bad Data?

It’s crucial that your company comes up with a viable, long-term strategy to rid your company of bad data. Of course, this is typically an intensive task and isn’t accomplished overnight. Most importantly, the removal of bad data isn’t simply a one-time task. It must be something that your data staff is continuously evaluating in order to stay in place and remain effective.

After an initial assessment of your company’s data processing practices and the volume of bad data you have, a professional firm can consult with your data team for technical strategies they can utilize in the future. By combining programmatic data input and output techniques with employee and company buy-in, no bad data problem is too out of control to squash.

Categories
Big Data Business Intelligence Data Analytics

Is Big Data the Key to Optimizing the Supply Chain?

One of the biggest challenges facing many companies is figuring out how to optimize their supply chains. For obvious reasons, they want to strike a balance between keeping costs down and making sure they have the resources required to continue to operate. As became evident during the early months of the COVID-19 outbreak, supply chains, especially global ones, can be tricky beasts to tame.

Maintaining the right balance between efficiency and resilience is challenging even in the best of economies. One solution many enterprises now use to stay nimble in the face of evolving circumstances is Big Data. 

By using computing power, algorithms, statistical methods, and artificial intelligence (AI), a company can condense the massive amount of available information about supply chains into comprehensible insights. That means making decisions quickly and without sacrificing optimization or resiliency. Let’s take a closer look at this trend and what it might mean for your operations.

What Can Big Data Do?

Computing resources can be focused on a handful of supply chain-related issues. These include jobs like:

  • Forecasting supply and demand
  • Proactive maintenance of infrastructure elements like warehouses and transportation
  • Determining how to best stow freight
  • Making pricing and ordering decisions
  • Inspecting items and identifying defects
  • Deploying workforce members, such as dockworkers and truck drivers, more efficiently

Suppose you run a consumer paper products company. You may need to scour the world for the best total price for a wood sourcing shipment. This may mean using Big Data systems to collect information about prices down the road and halfway across the world. Likewise, the company would need to make decisions about whether the costs of transporting and storing the wood pulp would be effective. Similarly, they’d need to establish confidence that each shipment would arrive on time.

How to Build the Needed Big Data Resources

First, it’s critical to understand that taking advantage of big data is about more than just putting a bunch of machines to work. A culture needs to be established from the top down at any organization. This culture has to:

  • Value data and insights
  • Understand how to convert insights into actions
  • Have access to resources like data pools, dashboards, and databases that enable their work
  • Stay committed to a continuous process of improvement

A company needs data scientists and analysts just as much as it needs computing power. C-level executives need to be onboarded with the culture, and they need to come to value data so much that checking the dashboards, whether it be on their phones or at their desk, is a routine part of their duties. Folks involved with buying, selling, transporting, and handling items need to know why supplies are dealt with in a particular way.

In addition to building a culture, team members have to have the right tools. This means computer software and hardware that can process massive amounts of data, turn it into analysis, and deliver the analysis as insights in the form of reports, presentations, and dashboards. Computing power can be derived from a variety of sources, including servers, cloud-based architectures, and even CPUs and GPUs on individual machines. 

Some companies even have embraced edge intelligence. This involves using numerous small devices and tags to track granular data in the field, at the edge of where data gathering begins. For example, edge intelligence can be used to track the conditions of crops. Companies in the food services industries can then use this data to run predictive analysis regarding what the supply chain will look like by harvest time.

What Are the Benefits?

Companies can gain a number of benefits from embracing Big Data as part of their supply chain analysis. By studying markets more broadly, they can reduce costs by finding suppliers that offer better rates. Predictive systems allow them to stock up on key supplies before a crunch hits or let slack out when the market is oversupplied. Tracking customer trends makes it easier to ramp up buying to meet emerging demand, driving greater profits.

Developing Big Data operations separates good businesses from great ones. With a more data-driven understanding of the supply chain, your operation can begin finding opportunities rather than reacting to events. By putting Big Data resources in place, supply chain processes can become more optimized and resilient.

Categories
Business Intelligence Data Visualization

The 3 Key Pillars to Better Dashboard Design

How you design your dashboard is crucial when it comes to displaying your data effectively. It’s important to visualize your data in a way that’s clear and easy for viewers to understand. However, with the abundance of data and reports needed to answer queries, it can be difficult to know what to consider in your design process. Let’s dive into the three key elements to implement when improving your dashboard design.

1. Develop a Plan

It’s natural to want to play around with your data and jump right into building dashboards. Nevertheless, when beginning, try not to start creating and adding charts right off the bat. It’s useful to plan ahead and layout the details of your dashboard prior to actually constructing it. This means determining the overarching purpose of your dashboard as well as what information needs to be included. Planning ahead will help to minimize overcrowding and continual adjustments to your design later on. 

What Should Go Where?

Thinking about the user’s experience when viewing a dashboard is essential when it comes to deciding where specific information should go. Here are a few things to think about when determining your initial dashboard design plan.

Placement

There is only one thing to be said about placement: location, location, location. While your dashboard is far from the real estate sector, consider that users will naturally give more attention to the left side of the screen. According to a recent eye-tracking study, users spend 80% of their time viewing the left side of the screen and only 20% viewing the right. 

Specifically, users were found to look to the top left corner of the screen the most, making this section of your dashboard prone to increased amounts of attention. The most utilized graphs and metrics should be placed in this portion of your dashboard, or any additional visualizations you deem significant. 

Don’t Hide Things

Similar to the point above regarding placement, you want to prioritize key information and make sure it’s easily found. You can’t expect much work from your end viewer to dive deeper than the surface data presented. Any additional clicking or scrolling required to find information is unlikely to be discovered by viewers. 

All things considered, an easy way to solidify your plan would be to create a rough draft either on paper or in any design application. This will allow you to play around with your placement and take a deeper dive into how certain elements complement each other.

2. Sometimes Less is More

We’ve all heard the common phrase that sometimes “less is more,” and dashboard design is no exception to this philosophy. You want your dashboard to be clear, concise, and easy to read. Avoid including too many charts and any unnecessary information. While an abundance of charts and graphs might appeal to the data driven enthusiast in you, they might be difficult for other viewers to read and understand. Minimizing the amount of data presented will prevent your audience from feeling overwhelmed due to information overload.

Choosing the Right Data Visualization

Choosing the most effective visualization for your data plays a key role in your dashboard’s simplicity. This is dependent on the type of data you are trying to visualize. Are you working with percentages? Data over a specific period of time? Are there any relationships present that you are trying to convey? 

The many variables that make up your data will affect your ultimate choice in visualization. Be sure to consider characteristics such as time, dates, hierarchies, and so on. 

3. Keep the End Viewer in Mind

Your audience is just as critical to your dashboard’s design as the information being presented. It’s important to always keep the end viewer in mind and understand how they are actually using the presented information.

When determining the characteristics of your end viewer, ask yourself questions such as:

  • Who will be viewing this dashboard on a daily basis?
  • How often do my viewers work with the type of data being presented? 
  • How will my audience be viewing this dashboard? Will viewers be sharing it as a pdf?

The answers to these questions will help you determine how much descriptive information to include alongside your visualizations.

Overall, there are numerous elements to consider when it comes to developing your business dashboards. It’s vital to always keep your audience in mind and plan ahead. Consider these key tips next time you’re building a new dashboard for improved design.   

Categories
Big Data Business Intelligence Data Analytics

5 Strategies to Increase User Adoption of Business Intelligence

Companies are turning to new strategies and solutions when it comes to using their data to drive decisions. User adoption is essential to unlocking the value of any new tool, especially in the field of business intelligence. However, like with most things, people are commonly resistant to change and often revert back to their original way of doing things. So how can organizations avoid this problem? Let’s explore five strategies that will help to effectively manage change and increase user adoption of business intelligence. 

Closely Monitor Adoption

It’s no secret that people are hesitant when introducing new tools and processes. If you don’t keep a close eye on the transition to a new tool, users will likely continue to use outdated methods such as disparate and inaccurate spreadsheets. Make sure those involved are working with the solution frequently and in the predetermined capacity. If you notice a few individuals rarely using the tool, reach out to discuss their usage as well as any concerns they might have surrounding the business intelligence solution. 

Top-Down Approach

Another strategy to increase user acceptance is the top-down approach. Buy-in from executives and senior stakeholders is crucial to fostering adoption, whether it be throughout your team or the entire organization. 

Consider bringing on an executive to champion the platform. This will empower other end-users to utilize the tool and recognize its overarching importance to the business moving forward. Leadership should also communicate heavily the why behind moving to a new solution. This will align stakeholders and help them to understand the transition as a whole.  

Continuous Learning & Training

Training is key to the introduction of any new processes or solutions. But you can’t expect your employees to be fully onboarded after one intensive training session. Try approaching the onboarding process as a continuous learning opportunity.

Implement weekly or bi-weekly meetings to allow everyone involved to reflect on what they’ve learned and collectively share their experience. Additionally, allotting time for regular meetings will give people the chance to ask questions and troubleshoot any possible problems they’ve encountered. 

Finding Data that Matters

Demonstrate the power of using data to drive decision making by developing a business use case. This application will allow you to establish the validity of the BI solution and show others where it can contribute value across business units. Seeing critical business questions answered will assist in highlighting the significance of the tool and potentially spark other ideas across users.

Remove Alternatives

A more obvious way to increase adoption is to remove existing reports or tools that users could possibly fall back on. Eliminating alternatives forces users to work with the new solution and ultimately familiarize themselves with the new dashboards.

Conclusion

Overall, there are many effective strategies when it comes to increasing user adoption. The downfall of many companies when it comes to introducing new solutions is their focus on solely the technical side of things. The organizational change and end-user adoption are just as crucial, if not more important, to successful implementation. Consider these approaches next time you’re introducing a new business intelligence solution. 

Categories
Big Data Business Intelligence

ETL vs. ELT: Critical Differences to Know

ETL and ELT are processes for moving data from one system to another. Both processes involve the same 3 steps, Extraction, Transformation, and Loading. The fundamental difference between the two lies in the order in which the data is loaded into the data warehouse and analyzed.

What is ETL?

ETL has been the traditional method for data warehousing and analytics. It is used to synthesize data from more than one source in order to build a data warehouse or data lake. First, the data is extracted from RDBMS source systems, which is the extraction stage. Next is the transformation stage, where all transformations are applied to the extracted data, and only then is it loaded into the end-target system to be analyzed by business intelligence tools.

What is ELT?

ELT involves the same three steps as ETL, but in ELT, the data is loaded immediately after extraction, before the transformation stage. With ELT, all data sources are aggregated into a single, centralized repository. With today’s cloud based data warehouses being scalable and separating storage from compute resources, ELT makes more sense for most modern businesses. ELT allows for unlimited access to all of your data by multiple users at the same time, saving both time and effort.

Benefits of ELT

Simplicity: Transformations in the data warehouse are generally written in SQL, which is the traditional language for most data applications. This means that anyone who knows SQL can contribute to the transformation of the data.

Speed: All of the data is stored in the warehouse and will be available whenever it is needed. Analysts do not have to worry about structuring the data before loading it into the warehouse. 

Self service analytics: When all of your data is linked together in your data warehouse you can then easily use BI tools to drill down from an aggregated summary of the data to the individual values underneath.

Bug Fixes: If you discover any errors in your transformation pipeline, you can simply fix the bug and re-run just the transformations with no harm done. With ETL however, the entire process would need to be redone.

Categories
Big Data Data Analytics Data Preparation

Data Wrangling vs. Data Cleaning: What’s the Difference?

There are many mundane tasks and time-consuming processes that data scientists must go through in order to prepare their data for analysis. Data wrangling and data cleaning are both significant steps within this preparation. However, due to their similar roles in the data pipeline, the two concepts are often confused with one another. Let’s review the key differences and similarities between the two as well as how each contributes to maximizing the value of your data.

What is Data Wrangling?

Data wrangling, also referred to as data munging, is the process of converting and mapping data from one raw format into another. The purpose of this is to prepare the data in a way that makes it accessible for effective use further down the line. Not all data is created equal, therefore it’s important to organize and transform your data in a way that can be easily accessed by others.

While an activity such as data wrangling might sound like a job for someone in the Wild West, it’s an integral part of the classic data pipeline and ensuring data is prepared for future use. A data wrangler is a person responsible for performing the process of wrangling.

Benefits of Data Wrangling

Although data wrangling is an essential part of preparing your data for use, the process yields many benefits. Benefits include:

  • Enhances ease of access to data
  • Faster time to insights
  • Improved efficiency when it comes to data-driven decision making

What is Data Cleaning?

Data cleaning, also referred to as data cleansing, is the process of finding and correcting inaccurate data from a particular data set or data source. The primary goal is to identify and remove inconsistencies without deleting the necessary data to produce insights. It’s important to remove these inconsistencies in order to increase the validity of the data set.

Cleaning encompasses a multitude of activities such as identifying duplicate records, filling empty fields and fixing structural errors. These tasks are crucial for ensuring the quality of data is accurate, complete, and consistent. Cleaning assists in fewer errors and complications further downstream. For a deeper dive into the best practices and techniques for performing these tasks, look to our Ultimate Guide to Cleaning Data.

Benefits of Data Cleaning

There is a wide range of benefits that come with cleaning data that can lead to increased operational efficiency. Properly cleansing your data before use leads to benefits such as:

  • Elimination of errors 
  • Reduced costs associated with errors
  • Improves the integrity of data
  • Ensures the highest quality of information for decision making

When comparing the benefits of each, it’s clear that the goals behind data wrangling and data cleaning are consistent with one another. They each aim at improving the ease of use when it comes to working with data, making data-driven decision making faster and more effective as a result.

What’s the Difference Between Data Wrangling and Data Cleaning?

While the methods might be similar in nature, data wrangling and data cleaning remain very different processes. Data cleaning focuses on removing inaccurate data from your data set whereas data wrangling focuses on transforming the data’s format, typically by converting “raw” data into another format more suitable for use. Data cleaning enhances the data’s accuracy and integrity while wrangling prepares the data structurally for modeling. 

Traditionally, data cleaning would be performed before any practices of data wrangling being applied. This indicates the two processes are complementary to one another rather than opposing methods. Data needs to be both wrangled and cleaned prior to modeling in order to maximize the value of insights.

Categories
Big Data Business Intelligence Data Analytics

Relational vs. Multidimensional Databases: Why SQL Can Impair Your Analytics

What is a Relational Database?

A relational database is a type of database that is based on the relational model. The data within a relational database is organized through rows and columns in a two-dimensional format.

The relational database has been used since the early 1970s, and is the most widely used database type due to its ability to maintain data consistency across multiple applications and instances. Relational databases make it easy to be ACID (Atomicity, Consistency, Isolation, Durability) compliant, because of the way that they handle data at a granular level, and the fact that any changes made to the database will be permanent. SQL is the primary language used to communicate with relational databases.

Below is an example of a two dimensional data array. Each axis in the array is a dimension, and each entry within the dimensions is called a position.

Store Location Product 1 Product 2
New York 83 68
London 76 97
As you can see we have an X and a Y axis, with each position corresponding to a Product and a Store Location.

What is a Multidimensional Database?

A multidimensional database is another type of database that is optimized for online analytical processing (OLAP) applications and data warehouses. It is not uncommon to use a relational database to create a multidimensional database.

As the name suggests, multidimensional databases contain arrays of 3 or more dimensions. In a two dimensional database you have rows and columns, represented by X and Y. In a multidimensional database, you have X, Y, Z, etc. depending on the number of dimensions in your data. Below is an example of a 3-Dimensional Data Array represented in a relational table and in 3-D.

Item Store Location Customer Type Quantity
Product 1 New York Public 47
Product 2 New York Private 20
Product 1 London Public 36
Product 2 London Public 69
Product 1 New York Private 36
Product 2 New York Public 48
Product 1 London Private 40
Product 2 London Private 28

The third dimension we incorporated into our data is “Customer Type” which tells us whether our customer was public or private.

We can then add a fourth dimension to our data, which in this example is time. This allows us to keep track of our sales, giving us the ability to see how each product is selling in relation to each store location, customer type, and time.

What are the Advantages and Disadvantages of Relational Databases?

Advantages: 

Single Data Locations: A key benefit to using relational databases is that data is only stored in one location. This means that each department will pull the data from a single collective source, rather than each department having their own record of the same information. This also means that when data is updated by one department, that change is reflected across the entire system, so that everybody’s data is always updated.

Security: Certain tables can be made available only to who needs it, which means more security for sensitive information. For example, it is possible for only the shipping department to have access to client addresses, rather than making that information available tclient addresses, rather than making that information available to all departments.

Disadvantages:

Running queries: When it comes to running queries, the simplicity of relational databases comes to an end. In order to access data, complex joins of many tables may need to be made, and even simple queries may need to be structured in SQL by a professional.

Live System Environments: Running a new query, especially ones that use DELETE, ALTER TABLE, and INSERT, can be incredibly risky when using a live system environment. The slightest error can mess everything up across the entire system, leading to loss of time and productivity.

What are the Advantages and Disadvantages of Multidimensional Databases?

Advantages:

Similar Information is Grouped: All similar information is grouped into a single dimension, keeping things organized and making it easy to view or compare your data.

Speed: Overall, using a multidimensional database will be faster than using a relational database. It may take longer to set up your multidimensional database, but in the long run, it will process data and answer queries faster.

Easy Maintenance: Multidimensional databases are incredibly easy to maintain, due to the fact that data is stored the same way it is viewed: by attribute. 

Better Performance: A multidimensional database will achieve better performance than a relational database with the same data storage requirements. Database tuning allows for further increased performance. Although the database cannot be tuned for every single query, it is significantly easier and cheaper than tuning a relational database.

Disadvantages:

Complexity: Multidimensional databases are more complex, and may require experienced professionals to understand and analyze the data to the fullest extent.

Categories
Artificial Intelligence Big Data

Big Data on the Big Screen: Top 5 Movies on Big Data & AI

Movies serve as a medium to convey and bring life to complex topics in a new way. Big Data and artificial intelligence have long been favorites of Hollywood and remain the focus of many feature films. While the big screen tends to exaggerate certain aspects of technology for its cinematic value, some truth remains behind the elements of AI and data science within these films. Let’s examine 5 top movies that explore the topics of Big Data and AI to add to your watchlist.

Moneyball

Brad Pitt leads this film as the general manager for the Oakland A’s, a baseball team with the lowest budget for players in the league. With the salary constraints to acquiring new players, the team looks to gain a competitive edge through statistical analysis. Data is at the core of management’s decision-making process when it comes to choosing key players and maximizing their budget. This film emphasizes the real-world application of predictive analytics and statistics when it comes to decision-making. 

Blade Runner

This classic film takes place in a world where artificially intelligent robots are created to serve society and work in off-world colonies. It is difficult to distinguish these robots, also referred to as “replicants,” from real humans. They are ruled illegal on Earth due to their lack of emotion, overpowering strength, and the danger they pose towards society. When four replicants manage to sneak onto Earth, they are hunted down by Rick Deckard, a resident Blade Runner. AI plays an integral role in this film as it tackles difficult conversations around humanity’s relationship with artificial intelligence and the ethical dilemmas that come with creating such machines. 

I, Robot

In a futuristic world, robots are engrained in the daily lives of humans, working as their assistants and serving their every need. The robots are programmed to follow the “Three Laws of Robotics” which are meant to protect society. However, this harmony is challenged when a supercomputer named VIKI (Virtual Interactive Kinetic Intelligence) violates these laws. VIKI sources and collects data from around the world in an effort to gain control of all robots. Here we have another representation of the age-old man vs. machine, but the uncertainty surrounding our ability to control the power of AI, even with rules in place, is highlighted.

Minority Report

Here data science is used by PreCogs, a team of “Data Scientists” operating in conjunction with the police, to predict precisely when and how future crimes will occur. Based on this, the police are able to arrest individuals before they’ve even committed a crime. Tom Cruise’s character, an officer in the PreCrime unit himself, is accused of a future murder and must prove he’s being framed. This film represents the real-world use of data to create social good and help make society better as a whole.

Her

This movie follows the relationship between Theodore Twombly, a lonely writer, and his AI-powered virtual assistant named Samantha. As a highly sophisticated operating system, Samantha can master large volumes of information and complete daily tasks for Theodore simultaneously. Her conversation skills are indistinguishable from that of another human, the witty banter and humorous remarks eventually evolve into a romantic connection between the two. This film portrays the potential complexities of the relationship between humans and AI-powered assistants as they become more advanced.

How to Learn from these Films

Though the primary goal of these films is to entertain and stimulate discussion amongst the audience, they each can help us learn important messages from the world of data science.

In order to dive deeper into the underlying themes and messages of these films, try the following:

  • Read film analyses and discussion forums online
  • Take time to reflect on your experience
  • Discuss the film with a friend or coworker
  • Research ideas and theories presented in the film

Overall, there are many opportunities to learn from these films and gain a deeper perspective on the power of data science. From real-world applications of predictive analytics to tackling the ethics of AI, movies have an interesting way of bringing life to these topics. Add any of these films to your watchlist to see for yourself!

Categories
Big Data Business Intelligence Data Analytics

DataOps 101: Why You Can’t Be Data-Driven Without DataOps

It’s no secret that data is becoming more and more central to every organization. Companies are investing heavily in their IT infrastructure as well as recruiting top talent to maximize every effort in becoming data-driven. However, most companies are still missing one key component from their data initiatives: DataOps.

DataOps isn’t necessarily new, many organizations already possess various elements and processes that fall under the philosophy without knowingly labeling them as DataOps. But many questions come to mind when the topic of DataOps is introduced. What is it? Why is it important? How is it different from the way you’re already working with data? Let’s address these questions and take a deep dive into why DataOps is essential to becoming truly data-driven.

What is DataOps?

While DataOps isn’t confined to one particular definition or process, DataOps is the culmination of many tools and practices in order to produce high-quality insights and deliverables efficiently. In short, the overarching goal is to increase the velocity of analytics outcomes in any particular organization while also fostering collaboration. Similar to DevOps, it’s built on the foundation of taking an iterative approach to working with data.

Why is DataOps Important?

In today’s fast-paced business climate, the quicker you can respond to changing situations and make an informed decision the better. The end-to-end process, though, when working with data can be quite extensive for many data science teams. Having systems in place to decrease the amount of time spent working with data anywhere in the process from data prep to modeling can promote operational efficiency. This improves the use of data to drive decisions across teams and the organization as a whole.

Furthermore, DataOps is all about improving how you approach data, especially with the high volumes of data being created today. This enhanced focus when working with data can lead to:

  • Better decision making
  • Improved efficiency
  • Faster time to insights
  • Increased time for experimentation
  • Stronger data-driven culture

Maximizing Time and Resources

Companies have an abundance of data to work with, but extracting value from it first requires data scientists to perform many mundane but necessary tasks in the pipeline. Finding and cleaning data is notorious for taking up too much time. The 80/20 Rule of Data Science indicates that analysts spend around 80% of their time sourcing and preparing their data for use, leaving only around 20% of their time for actual analysis. Once the data has been prepped, data scientists will then model and test before deployment. Those insights then need to be refined and communicated to stakeholders, often through the use of visualization tools.

This brief description of the analytics lifecycle is not entirely exhaustive as well, there are many additional steps that go into orchestration. But with no centralized processes in place, it’s likely that these tasks aren’t being performed in the most efficient way possible, making time to insights a lengthier cycle overall. The main point here is to emphasize the importance of DataOps in maximizing available time and resources. Adding automation and streamlining these tasks can increase your overall analytics agility.

Unifying Business Units

Additionally, DataOps helps unify seemingly disconnected business units and the organization as a whole. Having centralized practices and robust automation allows for less division or infrastructure gaps amongst teams. This can lead to greater creativity and innovation across business units when it comes to working with analytics.

Conclusion

There’s no question that the business value of data can be transformative to an organization. You don’t need to hire a whole new team, chances are you already have the core players needed to realize DataOps in your current operations. DataOps is more about producing these data and analytics deliverables quickly and effectively, increasing operational efficiency overall. If you’re serious about becoming data-driven, you should start thinking about adding DataOps to your data management strategy.

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