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Predictive Analytics in Manufacturing – How It Works [Use Case]

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Predictive analytics for manufacturing


Manual operations in manufacturing often lead to increased costs and decreased growth. Manufacturers have to resolve 4 critical challenges: operations optimization, cost savings, production quality improvement, and demand forecasting.

Digitizing one or two processes can only work to an extent and only a complete digital solution could come in handy. Especially, critical challenges like demand forecasting require a robust prediction system based on operation data analysis and without this manufacturers can never plan for the future.

Predictive Analytics in Manufacturing – Why it Matters and How it Works

So, what would be the best possible way to address these challenges?

An interesting yet best way to overcome this challenge is by automating the process with predictive maintenance solutions.

Let’s get started with the applications of predictive maintenance in manufacturing across improving operations and production quality at reduced cost and forecasting demand for the future in detail in the sections below.

What is predictive maintenance?

“Predictive maintenance (PdM) is maintenance that monitors the performance and condition of equipment during normal operation to reduce the likelihood of failures. Also known as condition-based maintenance, predictive maintenance has been utilized in the industrial world since the 1990s.

The goal of predictive maintenance is the ability to first predict when equipment failure could occur (based on certain factors), followed by preventing the failure through regularly scheduled and corrective maintenance.” (Source: Reliable Plant)

Manufacturing Predictive Analytics Market Outlook 2018 to 2026

“The manufacturing predictive analytics market size was valued at $535.0 million in 2018 and is projected to reach $2.5 billion by 2026, growing at a CAGR of 21.7% from 2019 to 2026. The advent of Industry 4.0 boosts substantive recent innovations in manufacturing.” (Source: Allied Market Research)

How the entire predictive maintenance system works

A predictive maintenance system comprises the Internet of Things (to collect data from any surface); Cloud (to process the data); Mobile applications (to push notifications based on data); AI/ML (to analyze and predict insights using data); web application (to share entire operations data under one roof).

The system works like this. Initially, the data will be gathered by IoT devices installed on machinery or assets.

The data will be processed in the Cloud or shared with the respective staff as notifications/warnings or alerts.

The processed data will be fed into the AI/ML system to analyze and predict the outcomes of the data accumulated over a certain period (generally historical data of at least 1 year is recommended).

The prediction reports will be shared with the respective stakeholders to make the necessary actions or decisions.

Image credit : Hakuna Matata Solutions

(Note: The image above illustrates how Predictive Maintenance works in a manufacturing plant)

Benefits of Predictive Maintenance for manufacturing

  • Capture condition-based real-time data collection accurately
  • Foresee & predict machine downtime early
  • Higher transparency
  • Reduced product delays
  • Improve planned production rate
  • Lower maintenance costs
  • Foresee machine failures
  • Reduce repair cost
  • Increase equipment’s life and utilization
  • Improve employee safety
  • Increased overall profits
  • Forecast demand

By now you’d have gathered Predictive Maintenance basics and its benefits.

Let’s dive deep into the discussion of how Predictive Maintenance is transforming manufacturing operations and growth.

Predictive maintenance for operation improvement

Operational efficiency plays a key role in the manufacturing production rate and quality. As this involves people, machines and technology, optimizing everything matters to enjoy a hassle-free production output matching the expected outcomes.

Before getting started with operations, it is a must to understand the challenges that impact operational efficiency.

It is a must to analyze the performance of machines operated at different levels (peak, medium or normal). The efficiency of the machines matters a lot when it comes to improving operational efficiency. Only if the machines are utilized to the fullest and perform to their best achieving maximum output is possible.

To accomplish this, it is a must to monitor the performance of every machine and its every movement possible. IoT is used to gather the data and based on the historic data analysis, the faults or inefficiencies in the operations are identified and rectified.

Not only that the problems that can arise in the future can be predicted with the IoT-enabled predictive maintenance system.

Generally, the OEE (overall equipment effectiveness) is calculated using the IoT data and this is analyzed and improved to make the overall operations efficient and rewarding.

“OEE = Availability * Performance * Quality”

Another scenario would be the performance of resources against the machines. It needs to be identified and fixed to improve staff efficiency. By digitalizing the process with Industry 4.0 solutions like IoT, it is easier to improve the efficiency of the overall operation.

Predictive maintenance for machine utilization and management

Unplanned maintenance of machines costs dearer for most manufacturing companies and this needs to be monitored and controlled to achieve maximum outputs.

Malfunctions or faulty machines impact manufacturing in two ways – first, they will reduce production quality and second, they will incur frequent repair costs.

So, it is a must to find out a way to find the inefficiency in machines and improve their performance before an outage happens, costing you an arm and a leg.

With a predictive maintenance system, the data gathered from every movement of the machine will provide a significant volume of data which then can be analyzed using an AI/ML program to identify the faults and malfunctions of machines.

A predictive maintenance system provides data on the asset’s current condition, its availability, defect information to help you rethink your production plans.

With such an approach and data trends, foreseeing and predicting the machine failures early as possible which leads to lower maintenance repair and labor cost. This could potentially save millions for your business.

Predictive maintenance for production quality

Even though predictive maintenance or IoT doesn’t have a direct impact on the quality of production or its rate, the combination of these two elements can really create a big impact on the overall production on the floor significantly.

As the IoT can help in streamlining the machine, people and technology. A predictive maintenance system will take care of the improved efficiency of machines — expecting an improvement in the production quality and rate is never a challenge for manufacturers.

Predictive maintenance for demand forecasting

An exclusive advantage of predictive maintenance for manufacturers is demand forecasting.

As the manufacturers have tons of data but left with no insights, the process of improving and planning ahead always slips. With a predictive maintenance system in place, it is seamless to foresee what can be done in the years to come based on the historical data.

As the predictive maintenance system curbs the data silos and creates 100% transparency over the entire manufacturing plant, it is never impossible to realize the current position and what to expect in the future.

With a plan and knowing what to expect — the manufacturing executives can plan well in advance to meet the customer requirements. Not only that you can easily identify the efficiency of machines, staff, and repair costs to plan the future goals — which will be practical.

Predictive maintenance use case – Asset management

Predictive maintenance has a wide number of use cases in the manufacturing industry, especially in condition-based monitoring of assets.

There can be scenarios where assets will be operated under different temperatures and monitoring their performance for different conditions is a must to maintain the production quality and rate.

These kinds of assets should be monitored constantly to keep them in good shape and even minor malfunctions or defects can cost the company millions of dollars.

With a predictive maintenance system, monitoring the asset under different conditions is seamless and the historical data obtained will help in foreseeing the asset performance in the future and when it needs replacement or maintenance.

Predictive maintenance helps in finding out

  • When the asset needs replacement
  • When asset maintenance is required
  • How long it will be efficient
  • When it can fail
  • What is causing the failure
  • What is the risk associated with failure
  • Which maintenance would be practical for improving asset utilization

Predictive maintenance ROI

Putting a functional predictive maintenance program in place can yield remarkable results: a tenfold increase in ROI, 25%-30% reduction in maintenance costs, 70%-75% decrease of breakdowns, and 35%-45% reduction in downtime.

When savings are expressed per labor hour, predictive maintenance costs $9 hourly pay per annum while preventive maintenance costs $13 hourly pay per annum. (Source: Infoq.com)

Summary

From what we have discussed above, predictive analytics is a boon to manufacturers as this will reduce the maintenance cost while improving the operational efficiency and production quality and help you plan for future programs.

Predictive analytics is evolving and the latest addition to Predictive Analytics, Prescriptive analytics is gaining steam in the industrial landscape.

The latter is a sub-component of predictive analytics and provides data on what is causing equipment failure and recommendations to improve the failure or defect.

With too many companies investing in predictive maintenance systems, it is high time for you to decide to keep up with the competition. Get started now before one of your competitors does.

Gengarajan PV

Gengarajan PV

CEO at Hakuna Matata Solutions Pvt Ltd

Gengarajan PV is a CEO of Hakuna Matata Solutions, a leading digital transformation company. He has more than 14 years of experience in the Information Technology industry. He spends his time reading about new technologies in Manufacturing, Distribution & Logistics industries.

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How Alternative Data is Changing the Finance Sector

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How Alternative Data is Changing the Finance Sector


Alternative data has been touted as the future for various companies. Financial services companies have taken a particular interest in the field as it has the potential to either provide completely novel signals or improve existing investment strategies.

However, understanding the scale and importance of alternative data has always been challenging as businesses in the sector are often shrouded in mystery. Investing is extremely competitive as alpha often depends on the signal strength other companies can acquire.

Now, however, the veil has been lifted, even if slightly. Finally, there is enough data to understand how far alternative data and web scraping have entrenched themselves into the industry, allowing us to understand their importance.

What is alternative data and web scraping?

Alternative data is a negatively defined term meaning everything that is not traditional data. The latter is considered to be everything that’s published regularly according to regulations, government action, or other oversight. In other words, it’s all the data from statistics departments, financial reports, press releases, etc.

Since alternative data is defined negatively, it’s every information source that’s not traditional. While the definition is somewhat broad, alternative data does have its characteristics. Namely, it’s almost always unstructured, comes in various formats (i.e., text, images, videos), and often is extracted for a highly specific purpose.

Data acquisition is significantly more complicated because both the sources and the formats are varied. Data as a Service (DaaS) businesses can resolve most of the acquisition issues; however, finding one that holds the necessary information can be complex.

Web Scraping and in-house solutions in alternative data acquisition

Many companies turn to building in-house solutions for alternative data acquisition. One of the primary methods for doing so is called web scraping. In short, it’s a method of automating online public data collection by employing bots.

These solutions go through a starting set of URLs and download the data stored within. Most bots will also further collect any URLs stored on the page for continued crawling. As a result, they can blaze through many sources within seconds or minutes.

Collected data is then delivered and parsed for analysis. Some of it, such as pricing information, can be integrated into completely automated solutions. Other data, such as anything from which investment signals might be extracted, is analyzed manually by dedicated professionals.

Web scraping is shaping the financial services industry

As mentioned above, financial services and investment companies have taken a particular interest in web scraping earlier than nearly anyone else. These businesses thrive upon gaining an informational edge over their competitors or the market as a whole.

So, in some sense, it was no surprise when web scraping turned out to be a key player in the financial services industry. So we surveyed over 1000 decision-makers in the financial services industry across the US and UK regions to find out more about how data is being managed in these companies.

Image Credit: Oxylabs; Thank you!

 

While internal data, as expected, remains the primary source of insight for all decision-making, web scraping has nearly overtaken it in the financial services industry. Almost 71% of our respondents have indicated that they use web scraping to help clients make business decisions.

Web Scraping and Growth Tendencies

Other insights are even more illuminating. For example, while web scraping has shown clear growth tendencies, we didn’t expect 80% of the survey respondents to believe that the focus will shift towards it even more in the coming 12 months. Nevertheless, these trends indicate a clear intent to change the dominant data acquisition methods in the industry.

Finally, there’s reason to believe that the performance of web scraping is equally as impressive. There may have been reason to believe that the process of automated data collection is simply a byproduct of hype. Big data has been a business buzzword for the longest time, so it may seem that some of that emotion might have transferred to web scraping.

Implementing Web Scraping

However, those who have implemented web scraping do not seem to think it’s pure hype. Over a quarter of those who have implemented the process believe it has had the most significant positive impact on revenue. Additionally, nearly half (44%) of all respondents plan to invest in web scraping the most in the coming years.

Our overall findings are consistent across regions. As the US and UK are such significant players in the sector, the conclusions likely extend to global trends, barring some exceptions where web scraping might be trickier to implement due to legal differences.

The survey has only uncovered major differences in how web scraping is handled, not whether it’s worthwhile. For example, in the US, it’s rarely the case that compliance or web scraping itself would be outsourced (12% & 8%, respectively). On the other hand, the UK is much more lenient regarding outsourced departments (22% and 15% for outsourced compliance and outsourced web scraping, respectively).

Conclusion

While the way data is being managed in the financial services industry has been shrouded in mystery for many years, we’re finally getting a better glimpse into the trends and changes the sector has been undergoing. As we can see, web scraping and alternative data play a major role in shaping the industry.

Becoming the true first adopters of web scraping, however, I think, is only the beginning. Both the technology and the industry are still maturing. Therefore, I firmly believe we will see many new and innovative developments in data extraction and analysis in the finance sector, which novel web scraping applications will head.

Image Credit: Pixabay; Pexels; Thank you!

Julius Cerniauskas

CEO at Oxylabs

Julius Cerniauskas is Lithuania’s technology industry leader & the CEO of Oxylabs, covering topics on web scraping, big data, machine learning & tech trends.

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How to Implement a Splintered Content Strategy

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How to Use SEO if You Have No Experience


Content makes the marketing world go round. It doesn’t matter what your overarching marketing strategy looks like – content is the fuel source. You can’t go anywhere without it. The biggest problem is that content can be expensive to create. We operate in a business world where thousands of pieces of content are created every single second. Trying to keep up can feel like an expensive exercise in futility.

The key to successful digital marketing in an era of saturated online channels is extracting maximum value from your content. If the traditional approach is built around “single-use” content, you need to switch gears and opt for a multi-use approach that allows you to leverage the same content over and over again. One way to do this is by building out a “splintered” content strategy.

What is a Splintered Content Strategy?

The best way to understand the splintered approach to content creation is via an analogy. In the analogy, you start with one core topic that relates to your brand and readers. This topic is represented as a tree. Then, when you want to get more value out of the tree, you chop it down into big logs. These logs represent sub-topics of more significant topics. These logs can then be split and broken down into even smaller niches. (And this process of splintering the original topic into smaller/different pieces of micro-content can go on and on.)

Content splintering is not to be confused with content republishing or duplication. The mission isn’t to reuse the same content so much as to extract more value from the original content by finding new uses, applications, angles, and related topics. Not only does this approach help you maximize your ROI, but it also creates a tightly-correlated and highly-consistent web of content that makes both search engines and readers happy.

What You’ll Need for a Splintered Content Strategy

In order to get started with creating splintered content, you’ll need a few things:

  • Keyword research. The process always begins with keyword research. First, you need to perform detailed SEO research to zero in on the keywords that specifically resonate with your target audience. This feeds your topic selection and actual content creation. (You can think of keyword research as developing a blueprint. Just like you can’t build a house without plans, you can’t implement a splintered content strategy without keyword research.)
  • General topic. Armed with the right keywords, you can begin the process of choosing a broad topic. A general topic is a very basic, overarching topic that speaks to a specific target audience.
  • Content writers. You’ll need a team of people to actually create the content. While it’s possible to do this on your own, you ideally want to hire content writers to do the heavy lifting on your behalf. This allows you to focus on the big-picture strategy.
  • Consistency. A splintered content strategy requires consistency. Yes, there are ways to automate and streamline, but you have to ensure that you’re consistently churning out content (and that the content is closely correlated).

A good splintered content strategy takes time to develop. So, in addition to everything mentioned above, you’ll also need patience and resilience. Watch what’s working, and don’t be afraid to iterate. And remember one thing: You can always splinter a piece of content into more pieces.

How to Plan and Execute a Splintered Content Strategy

Now that we’re clear on splintered content and some of the different resources you’ll need to be successful, let’s dig into the actual how-to by looking at an illustration of how this could play out. (Note: This is not a comprehensive breakdown. These are merely some ideas you can use. Feel free to add, subtract, or modify to fit your own strategy needs.)

Typically, a splintered content strategy begins with a pillar blog post. This is a meaty, comprehensive resource on a significant topic that’s relevant to your target audience. For example, a financial advisor might write a pillar blog post on “How to Sell Your House.” This post would be several thousand words and include various subheadings that drill into specific elements of selling a house.

The most important thing to remember with a pillar post is that you don’t want to get to micro with the topic. You certainly want to get micro with the targeting – meaning you’re writing to a very specific audience – but not with the topic. Of course, you can always zoom in within the blog post, and with the splinters it produces, but it’s much more difficult to zoom out.

  • Turn the Blog Post Into a Podcast Series

Once you have your pillar piece of content in place, the splintering begins. One option is to turn the blog post into a series of podcast episodes. Each episode can touch on one of the subheadings.

If these are the subheadings from the blog post, they would look like this:

  • How to prepare for selling > Episode 1
  • How to find a real estate agent > Episode 2
  • How to declutter and stage your property > Episode 3
  • How to price your property > Episode 4
  • How to choose the right offer > Episode 5
  • How to negotiate with repair requests > Episode 6
  • How to prepare for closing day > Episode 7
  • How to move out > Episode 8

Depending on the length of your pillar content, you may have to beef up some of the sections from the original post to create enough content for a 20- to 30-minute episode, but you’ll at least have a solid outline of what you want to cover.

  • Turn Podcasts Into YouTube Videos

Here’s a really easy way to multiply your content via splintering. Just take the audio from each podcast and turn it into a YouTube video with graphic overlays and stock video footage. (Or, if you think ahead, you can record a video of you recording the podcast – a la “Joe Rogan” style.)

  • Turn YouTube Videos Into Social Clips

Cut your 20-minute YouTube video down into four or five different three-minute clips and soundbites for social media. These make for really sticky content that can be shared and distributed very quickly.

  • Turn Each Podcast Into Long-Form Social Posts

Take each podcast episode you recorded and turn them into their own long-form social posts. Of course, some of this content will cover information already hashed out in the original pillar post, but that’s fine. As long as you aren’t duplicating content word-for-word, it’s totally fine if there’s overlap.

  • Turn Long-Form Social Posts Into Tweets

Your long-form social posts can then be turned into a dozen or more individual short-form tweets. Find the best sentences, most shocking statements, and most powerful statistics from these posts and schedule a series of automated posts to go out over a few weeks. (You can automate this process using a tool like Hootsuite or Buffer.)

  • Turn Content Into an Email Campaign

Finally, take your best content and turn it into a series of emails to your list. You may even be able to set up an autoresponder series that slowly drips on people with a specific call-to-action.

Using the example from this article, a real estate agent might send out a series of 10 emails over 30 days with a call-to-action to get a free listing valuation.

Take Your Content Strategy to the Next Level With Splintered Content Strategy

There isn’t necessarily a proper way to implement a splintered content strategy. But, like everything regarding marketing, there’s ample room for creativity.

Conclusion

Use the parts of this article that resonate with you and adapt the rest to fit your vision for your content. Just remember the core objective of this entire approach: content maximization.

The goal is to get the most value out of your content as possible. And you do that by turning each piece of content you create into at least one more piece of content. If you do this efficiently, you will be successful.

Image Credit: by Kampus Production; Pexels; Thank you!

Timothy Carter

Chief Revenue Officer

Timothy Carter is the Chief Revenue Officer of the Seattle digital marketing agency SEO.co, DEV.co & PPC.co. He has spent more than 20 years in the world of SEO and digital marketing leading, building and scaling sales operations, helping companies increase revenue efficiency and drive growth from websites and sales teams. When he’s not working, Tim enjoys playing a few rounds of disc golf, running, and spending time with his wife and family on the beach — preferably in Hawaii with a cup of Kona coffee. Follow him on Twitter @TimothyCarter

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Successful AI Requires the Right Data Architecture – Here’s How

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Successful AI Requires the Right Data Architecture - Here’s How


For companies that can master it, Artificial Intelligence (AI) promises to deliver cost savings, a competitive edge, and a foothold in the future of business. But while the rate of AI adoption continues to rise, the level of investment is often out of kilter with monetary returns. To be successful with AI you’ll want the right data architecture. This article tells you how.

Currently, only 26% of AI initiatives are being put into widespread production with an organization. Unfortunately, this means many companies spend a lot of time on AI deployments without seeing tangible ROI.

All Companies Must Perform Like a Tech Company

Meanwhile, in a world where every company must perform like a tech company to stay ahead, there’s increasing pressure on technical teams and Engineering and IT leaders to harness data for commercial growth. Especially as spending on cloud storage increases, businesses are keen to improve efficiency and maximize ROI from data that are costly to store. But unfortunately, they don’t have the luxury of time.

To meet this demand for rapid results, mapping data architecture can no longer stretch on for months with no defined goal. At the same time, focusing on standard data cleaning or Business Intelligence (BI) reporting is regressive.

Tech leaders must build data architecture with AI at the forefront of their objectives.

To do otherwise — they’ll find themselves retrofitting it later. In today’s businesses, data architecture should drive toward a defined outcome—and that outcome should include AI applications with clear benefits for end-users. This is key to setting your business up for future success, even if you’re not (yet) ready for AI.

Starting From Scratch? Begin With Best Practices for Data

Data Architecture requires knowledge. There are a lot of tools out there, and how you stitch them together is governed by your business and what you need to achieve. The starting point is always a literature review to understand what has worked for similar enterprises, as well as a deep dive into the tools you’re considering and their use cases.

Microsoft has a good repository for data models, plus a lot of literature on best data practices. There are also some great books out there that can help you develop a more strategic, business-minded approach to data architecture.

Prediction Machines by Ajay Agarwal, Joshua Gans, and Avi Goldfarb is ideal for understanding AI at a more foundational level, with functional insights into how to use AI and data to run efficiently. Finally, for more seasoned engineers and technical experts, I recommend Designing Data-Intensive Applications by Martin Kleppmann. This book will give you the very latest thinking in the field, with actionable guidance on how to build data applications, architecture, and strategy.

Three Fundamentals for a Successful Data Architecture

Several core principles will help you design a data architecture capable of powering AI applications that deliver ROI. Think of the following as compass points to check yourself against whenever you’re building, formatting, and organizing data:

  • Building Toward an Objective:

    Always have your eye on the business outcome you’re working toward as you build and develop your data architecture is the cardinal rule. In particular, I recommend looking at your company’s near-term goals and aligning your data strategy accordingly.

    For example, if your business strategy is to achieve $30M in revenues by year-end, figure out how you can use data to drive this. It doesn’t have to be daunting: break the more important goal down into smaller objectives, and work toward those.

  • Designing for Rapid Value Creation:

    While setting a clear objective is key, the end solution must always be agile enough to adapt to changing business needs. For example, small-scale projects might grow to become multi-channel, and you need to build with that in mind. Fixed modeling and fixed rules will only create more work down the line.

    Any architecture you design should be capable of accommodating more data as it becomes available and leveraging that data toward your company’s latest goals. I also recommend automating as much as you can. This will help you make a valuable business impact with your data strategy quickly and repeatedly over time.

    For example, automate this process from the get-go if you know you need to deliver monthly reporting. That way, you’ll only spend time on it during the first month. From there, the impact will be consistently efficient and positive.

  • Knowing How to Test for Success:

    To keep yourself on the right track, it’s essential to know if your data architecture is performing effectively. Data architecture works when it can (1) support AI and (2) deliver usable, relevant data to every employee in the business. Keeping close to these guardrails will help ensure your data strategy is fit for purpose and fit for the future.

The Future of Data Architecture: Innovations to Know About

While these key principles are a great starting place for technical leaders and teams, it’s also important not to get stuck in one way of doing things. Otherwise, businesses risk missing opportunities that could deliver even greater value in the long term. Instead, tech leaders must constantly be plugged into the new technologies coming to market that can enhance their work and deliver better outcomes for their business:

  • Cheaper Processing:

    We’re already seeing innovations making processing more cost-efficient. This is critical because many of the advanced technologies being developed require such high levels of computer power they only exist in theory. Neural networks are a prime example. But as the required level of computer power becomes more feasible, we’ll have access to more sophisticated ways of solving problems.

    For example, a data scientist must train every machine learning model. But in the future, there’s potential to build models that can train other models. Of course, this is still just a theory, but we’ll definitely see innovation like this accelerate as processing power becomes more accessible.

  • Bundled Tools:

    Additionally, when it comes to apps or software that can decrease time to value for AI, we’re in a phase now where most technology available can only do one thing well. The tools needed to productionize AI — like storage, machine learning providers, API deployment, and quality control — are unbundled.

    Currently, businesses risk wasting precious time simply figuring out which tools they need and how to integrate them. But technology is gradually emerging that can help solve for multiple data architecture use cases, as well as databases that are specialized for powering AI applications.

    These more bundled offerings will help businesses put AI into production faster. It’s similar to what we’ve seen in the fintech space. Companies initially focused on being the best in one core competency before eventually merging to create bundled solutions.

  • Data Marts vs. Data Warehouses:

    Looking further into the future, it seems safe to predict that data lakes will become the most important AI and data stack investment for all organizations. Data lakes will help organizations understand predictions and how best to execute those insights. I see data marts becoming increasingly valuable for the future.

    Marts deliver the same data to every team in a business in a format they can understand. For example, Marketing and Finance teams see the same data represented in metrics that are familiar and – most importantly – a format they can use. The new generation of data marts will have more than dimensions, facts, and hierarchy. They won’t just be slicing and dicing information — but will support decision-making within specific departments.

Conclusion

As the technology continues to develop, it’s critical that businesses stay up to speed, or they’ll get left behind. That means tech leaders staying connected to their teams, and allowing them to bring new innovations to the table.

Even as a company’s data architecture and AI applications grow more robust, it’s essential to make time to experiment, learn and (ultimately) innovate.

Image Credit: by Polina Zimmerman; Pexels; Thank you!

Atul Sharma

Atul founded Decision Intelligence company Peak in 2015 with Richard Potter and David Leitch. He has played a pivotal role in shaping Peak’s Decision Intelligence platform, which emerged as an early leader in a category that is expected to be the biggest technology movement for a generation. Peak’s platform is used by leading brands including Nike, Pepsico, KFC and Sika.
On a mission to change the way the world works, the tech scaleup has grown quickly over the last seven years and now numbers over 250 people globally. Regularly named a top place to work in the UK, this year Peak received the Best Companies 3-star accreditation, which recognizes extraordinary levels of employee engagement.
Prior to Peak, Atul spent over 20 years working in data architecture and data engineering. He has worked on designing and implementing data integration and data warehouse engagements for global companies such as Morrisons Plc, The Economist, HBOS, Admin Re (Part of Swiss Re) and Shell.

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