A 3 Phase Plan for Sustainable Digital Transformation

“The hardest part is starting. Once you get that out of the way, you’ll find the rest of the journey much easier.”

Simon Sinek

According to a McKinsey Global Survey, more than 60 percent of respondents with stalled digital transformations attribute the problem to factors that organizations can control. This notion goes against widespread assumptions that external pressures, such as market disruptions or regulatory changes, pose the most significant threats to digital initiatives. More commonly, sources of derailed progress included lack of clarity or alignment on a company’s digital strategy and poor quality of the digital strategy to begin with.

Cultivating A “Marathon, Not A Sprint” Mentality

It’s incumbent upon the digital transformation leaders to manage expectations and take the lead in defining realistic, data-driven ambitions for the enterprise. It’s also critical for all stakeholders to agree upon the necessary governance changes to achieve the new objectives.

Re-envisioning your digital transformation project requires crafting a bold, inspiring mission statement – one that is clear, concise, and consistent with established strategic pillars. This will help everyone focus their efforts on building a program that delivers immense value. Pick a leader who can cast a compelling vision, which acknowledges the past but establishes a new and exciting future.

It’s important to understand and accept that your other project and business leaders will shape the basic perceptions associated with your subsequent plans and actions.Therefore, proper preparation, assessment, planning, acting, measuring, and above all, communication can greatly enhance your chances of success.

Implementing a Proven Methodology and Framework

Following a proven approach to reinvigorate your data roadmap will likely solve many of the problems faced initially. There are a number of different frameworks that can help design your approach. For example, the Prosci change management framework is one approach to following a structured process for implementing lasting change within an organization. It’s initial strategic framework includes three phases: 

  • Phase 1 – Prepare Approach
  • Phase 2 – Manage Change
  • Phase 3 – Sustain Outcomes

Each phase is broken down into three stages, and each stage includes important activities to support the success of a change. Similar to other change management methodologies, the Prosci approach is structured, yet also adaptable and scalable to fit the needs of any organization or change initiative. For more information visit the Prosci 3-Phase Process full outline.

Source: © Prosci, Inc. – Managing Change: Take Action and Implement Plans Worksheet

Conclusion

Again, the Prosci methodology is just one of many available to successfully manage change to support digital transformation. See this list of 10 other models for a few examples. Regardless of the exact framework, what they all have in common is taking a methodical approach to ensure your digital transformation efforts are lasting – meaning you’ve identified the right goals and that you have stakeholders and an organization that’s fully invested in the change.

Don’t let fear of failure get in the way of trying to move your digital transformation efforts forward. With structure, clarity, and concrete objectives, leaders can forge a new path and create a new momentum that allows for a data-driven culture to emerge and thrive.

5 Digital Transformation Lessons from Dune

Last week, sci-fi fans finally got to see the latest film adaptation of Dune. When published in 1965, Frank Herbert’s novel was a groundbreaking, eco-conscious sci-fi epic. Set 20,000 years in the future with intergalactic dynasties and secret orders battling for control of the scarcest resource in the universe, Dune seems both completely alien but also very familiar.

Much has been written about Herbert’s inspiration for dune. But while the author had plenty of history and his own time to draw from, the story is even more relevant today, given how dire some of the same issues have become. So if Dune does such a great job of reflecting our current situation, what insights can it offer into how to address our challenges?

Here are 5 lessons from Dune on digital transformation

1. Bring back the thinking machines

In the Dune universe, a war against machines results in a prohibition against AI or “machines in the likeness of a human mind.” Subsequently, over thousands of years, humanity has filled the role of advanced computers with Mentats. After undergoing conditioning at specialized schools, these ‘human computers’ are able to process large amounts of data, identify patterns, apply logic, and then deduce probable future outcomes. The prescience and strategic abilities of Mentats make them valued advisors, with the great houses of the universe vying for their service.

Atreides Mentat Thufir Hawat

Sound familiar? With organizations across all industries racing to capitalize on AI, there’s been growing demand for data science related roles. Companies have to compete with big tech companies for talent, and there is simply not enough supply to meet the demand.

The solution? Automation. “Many machines on Ix. New machines,” notes a guild navigator (another class of humans that replace the work formerly handled by computers). Organizations can automate much of their data science work by partnering with vendors that have already made significant investments in R&D and data science talent. Leveraging outside expertise to focus on improving specific workflows is more cost-effective, provides flexibility, and can accelerate digital transformation efforts. 

It’s time to bring back the thinking machines (spoiler alert: the humans and AI eventually make peace in the Dune series).

2. Every drop counts

In contrast to the Harkonnen who seem to indulge in daily steam showers, the Fremen natives of Dune are relentless in their conservation of water. Donning water-preserving suits, the Fremen even reclaim water from corpses and avoid crying. Of course, personal survival demands it, but their hyper-vigilant water preservation also serves their long term vision – terraforming their desert planet into a green oasis. The Fremen use wind traps to collect moisture from the air and slowly amass giant caches of water across thousands of sites.

Fremen water catch basin

Organizations rightly prioritize opportunities that promise to have the biggest impact. But they also shouldn’t overlook less obvious opportunities to innovate (for instance, optimizing the various points at which water is used within food manufacturing processes). By applying the same rigor across other processes, the many small gains in aggregate can have an enormous impact on the efficiency and sustainability of the entire business.

3. “The slow blade penetrates the shield”

Combat in Dune highlights the value of adaptation and an incremental approach. With personal shielding technology having rendered conventional projectile weapons largely ineffective, military forces in Dune revive the use of hand-to-hand combat and traditional weapons. To win in battle, soldiers have to think steps ahead and employ techniques that allow them to overcome the shields, which only yield to slow attacks.

Likewise, with the conventional, top-down approach to digital transformation often failing to deliver, organizations must adapt more effective strategies. A survey of industrial professionals indicated that while 94% have taken an organization-wide approach to digital transformation, only 29% claimed success. Stymied by unanticipated complexity and plagued with delays and cost overruns, many organizations are turning to an operation-specific approach to digital transformation. By implementing digitization and automation techniques to specific workflows first, organizations are able to ensure incremental success and then scale their efforts to the rest of the org.

4. Enlist the frontline

Another benefit of the ops-specific approach is that it more effectively involves and considers those closest to the processes being targeted. In Dune, as the management of Arrakis and spice mining changes hands from the Harkonnen to the Atreides, there’s a clear distinction in the management style of the Atreides. The Harkonnen impose their rule and maximize spice production with violent oppression. By contrast, the Atreides begin their management by sending envoys to engage the locals. They rescue spice harvester workers at the expense of spice production, and then Paul embeds himself with the Fremen and gains their desert knowledge. The approach pays off, as Paul is able to mobilize the locals to overwhelming success.

Similarly, it behooves organizations looking to transform their operations to enlist stakeholders at all levels, especially those that can assess the situation on the ground and identify all opportunities to innovate. Getting their buy-in, tapping their experience and expertise, and ensuring the project delivers on their goals will increase chances for success.

Dune spice miner

5. Fear is the mindkiller

“Moods are a thing for cattle and love play” declares 1984 Dune’s Gurney Halleck while chiding Paul Atreides for not being more vigilant in preparing for their hostile destination. Once on Arrakis, Paul finds himself stripped of his resources and stranded in the desert. He’s forced to quickly hone his skills and adapt to the conditions of his new environment. 

The pace of innovation across all industries is increasing. To maintain their competitive advantage, organizations must create an environment to support innovation within. They can’t afford to wait for years long, enterprise-wide digital transformation projects to deliver uncertain results. Budgetary limits, legacy systems, lack of expertise, and other challenges can be overcome with the right approach. The op-specific approach can help organizations adapt faster, empower professionals across the organization, and realize ROI sooner. 

The sleeper must awaken!

Zillow & 2 Attributes of A Successful Data Culture

In our recent e-book, 3 Hacks for Onboarding AI Platforms, we outline a few key steps to building the right team and culture to support an AI deployment. And we did so for good reason. There is broad consensus that the success of digital transformation efforts hinge on having a data-driven culture behind it. A 2019 Deloitte study found that companies with strong data-driven cultures were twice as likely to exceed business goals. Another study by New Vantage Partners found that 95% of the challenge to adoption of big data and AI initiatives was cultural, organizational, or process-driven rather than technological.

Given this, organizations have prioritized fostering data-driven cultures within their organizations. Whether it’s hiring a digital-focused executive, establishing centers of excellence, or instituting organization-wide mandates, the focus is on moving away from decisions based on gut feeling to those based on data-derived facts. 

Organizations Must Look Beyond the Numbers

Sounds great, but an effective data driven organization must often look beyond the numbers and can face major consequences when they fail to do so. Take for example Zillow, a company that has used data to not only build more accurate real estate models but has also leveraged data into a powerful competitive advantage.

Zillow’s automated home-buying business recently made headlines for its decision to halt home purchases. The company, which has access to more than 17 years worth of data, is hearing backlash after the announcement. Some are calling into question the company’s ability to properly plan and take into account logistical constraints. Others are wondering if their brand has been irreparably damaged. How could these things happen in a data-centric company?

Attributes of a Data-Informed Culture: Intuition & Ownership

In our experience, organizations have proven tremendously successful when they connect big data analytics to the business strategy. This data-informed approach means they acknowledge the data-derived insights but are also aware of and account for the implications of other non-data factors that may impact the direction of the overall strategy.

It also means that when building this data-informed culture, in addition to data literacy, organizations must also look for and encourage two key attributes: 1) Intuition and 2) Ownership

Intuition is defined as the natural ability to know something without any proof or evidence.But it’s also another data point, based on unconscious knowledge, expertise, and experience to be combined with other data in decision making. Ownership is the state of being responsible and accountable. It’s critical that these two components are embedded into the company’s values so that data may be used in a way that properly guides and informs decisions. Otherwise, you may be sitting on actionable insights that no one has evaluated properly or acted on because it’s “not my place.” Someone must answer to the choices being made and how those decisions align to and support broader goals.

It’s easy to wonder if the culture at Zillow didn’t empower the decision makers to use their intuition in the process, but instead they had been accustomed to letting the data be their one and only guide. 

It also highlights a gap between the company’s actions and the real-world issues having to do with the on-the-ground workers and supply constraints. This could be the result of a lack of ownership over the decisions being made.

Being data-informed in addition to data-driven means using both intuition and ownership to constantly check your assumptions, methods and outcomes. The qualitative complements the quantitative, just as the human element complements the data analysis. 

If you want to take your data insights to the next level and avoid the unintended consequences associated with mismanaging the intangible side of your business, look for people that demonstrate high intuition and ownership traits. Your culture will thank you for it.

AI Automation in Manufacturing: 5 Key Business Functions Being Trusted to AI

Business Insider predicts that by next year, manufacturers will spend approximately $267 billion on IoT technology. One of AI’s key roles in that massive investment is its ability to automate and manage the millions of micro-decisions that manufacturers have to make. This enables them to deliver high-quality goods that customers have come to expect. While AI in manufacturing is not a catch-all for every business challenge, there are many specific business functions manufacturers can easily automate for greater cost-efficiency, safety, and accuracy across the manufacturing process.

Here are five examples that show how AI-based decision support systems are automating tasks and helping alleviate the labor shortage in the manufacturing industry:

1. Equipment Maintenance

For factories and industrial operations that are expected to run 24/7, equipment downtime can be a major operational challenge. Whether it’s planned maintenance on a membrane in an industrial beverage plant or an emergency repair on a pipe in an oil well, operators need to be able to manage and react to equipment issues as fast as possible.

IoT sensors can monitor factors that affect equipment conditions across industries such as oil temperature, salinity levels, and vibration levels. IoT sensors can give operators critical insights into wear-and-tear as well as emergency issues, allowing them to shut down equipment to prevent catastrophic failure or take other appropriate actions. The speed of response is an important factor in minimizing the potential losses: unplanned downtime costs manufacturers an estimated $50 billion annually.

2. Quality Control

In an increasingly competitive market, manufacturers cannot afford to waste resources on subpar products. AI algorithms can proactively identify mistakes and abnormalities that can occur at any time along the production process.

There are business tasks that human workers will always be better suited for, but machines can be more appropriate to perform quality control tasks than manual inspectors are.

According to recent Mckinsey statistics, deep-learning-based systems can provide defect detection improvements up to 90% compared to a human inspector.

3. Supply Chain Optimization

AI’s contributions to the manufacturing sector don’t stop at the production line. Algorithms can help companies improve how they deliver their products to their consumers via predictive analytics. Better informed firms are able to shift from a reactionary model to a more profitable and predictive one.

IoT sensors can collect a myriad of data along the industrial supply chain, from transportation and energy consumption to raw material cost fluctuations to weather patterns and other market conditions that can have an impact on a company’s bottom line.

4. Time-Consuming Parts of the Design Process

While automation’s benefits can be clearly observed on the factory floor, AI also is helping streamline and optimize the design process in manufacturing. For small and incremental improvements to a product’s design, AI algorithms are able to explore millions of different tweaks and adjustments to a design to optimize its performance. Factors such as material usage and efficiency, structural strength, and weight can all be assessed and improved upon with AI algorithms.

5. Dangerous Manual Tasks

Safety is an inherent problem when workers and traditional machines are sharing the same space. While rare, accidents are always a possibility when workers are operating near powerful machines with no cognitive awareness.

Cobots are AI-powered collaborative machines designed to safely work alongside skilled laborers in industrial environments. Cobots can assume physical tasks such as heavy lifting and repetitive tasks that require a degree of fine-motor control. At an estimated cost of about $24,000 each, the machines can prove to be a useful and affordable supplement to skilled laborers on a factory floor.

While there is enormous potential in automation for the manufacturing industry, it’s not always an easy task to implement an AI project. Be on the lookout for our upcoming survey report that takes a closer look at the challenges faced by manufacturers and other business leaders when implementing AI initiatives in their businesses.

Follow us on Twitter and LinkedIn for more company updates or drop us a line through our website.

3 Ways Big Data and A.I. Will Positively Impact Agriculture

The Agricultural Revolution was one of the most defining moments in human history, providing opportunities for billions of people to live richer, healthier and more fulfilling lives. We owe almost everything we have today to a few innovative pioneers from tens of thousands of years ago who recognized the value in farming. 

But we shouldn’t view the agricultural revolution as a singular, stagnant event. With global food demand expected to rise by 59–98% between 2005 and 2050, and the realities of a harsher, less predictable climate becoming more apparent every day, we owe it to our ourselves as well as our Neolithic ancestors to continue to find ways to innovate the way we create and use resources to keep the world fed. 

At Plutoshift, we are particularly interested in the vital role artificial intelligence and big data will play in keeping farmers efficient, sustainable and profitable.

Here are three ways modern-day pioneers are unlocking their data and leveraging A.I. to deliver meaningful outcomes.

A.I. will provide actionable insights faster than ever

One of the biggest challenges modern farmers face is getting the biggest yield from their crops in a sustainable way in the face of  an unpredictable environment. Farmers in Africa, for example, lose an estimated 49% of their expected total crop yield per year to factors like pests, disease and water measurement issues. 

AI-powered applications can both measure and act on factors that affect yields, such as soil acidity, moisture levels, fertilizer application, and a farm’s risk of pest infestation. Farmers in Texas have used the power of AI to compare historical satellite images of farmland to accurately predict when a swarm of potentially devastating grasshoppers was making its way to crops, providing warning much earlier than ever possible before. 

A.I. and smart data use can aid sustainability 

Research shows that agriculture is responsible for up to a quarter of all human-caused greenhouse gas emissions. Additionally, irrigation for agriculture is responsible for 70-90% of the world’s freshwater usage. When combined with land and fertilizer usage, these factors take a significant toll on the world’s resources and environment. 

How then can we use technology to mitigate the cost to the environment, while still feeding a hungry world? 

In the case of irrigation, advances in A.I. applications are helping both large and small-scale farms use water more responsibly and efficiently. One organization, DHI GRAS, developed a solution to help farmers reduce water consumption in their fields via A.I. algorithms that use thermal and optical satellite data, combined with continually updated meteorological data.

With data-driven insights provided, farmers can use exactly the right amount of water for maximum yield while limiting waste. 

A.I. can refresh a struggling labor market

While agriculture was once the most popular industry worldwide, employment numbers have dwindled during the past few centuries. In fact, in 1870, almost 50 percent of the U.S. population was employed in agriculture. As of 2008, that number has dwindled to less than 2 percent. Thanks to automation and other sources of innovation that have increased yields per acre, it simply takes fewer workers to successfully run a farm than in years past. 

Data-driven farming is set to provide massive economic opportunities. It has been estimated that smarter farming practices could generate $2.3 trillion overall worldwide annually, with $250 billion coming from AI and data analytics alone.

With a significantly increased demand for food and richer dietary expectations awaiting the industry in the next few decades, data-driven solutions (and the skills to provide them) will be in high demand.  

Interested in learning more about how A.I. can lead to relevant and actionable data insights? Sign up for a demo of our easy-to-use platform here. Also, be sure to stay connected by following us on LinkedIn and Twitter.

Infographic – The Challenge of Turning Data Into Action Report

Manufacturing professionals are overwhelmed with data from remote sensors, connected devices and software from a myriad of systems.  The difficulty comes in analyzing the data efficiently and gaining immediately actionable insights to improve processes and drive costs down.

We surveyed 500 mid-level manufacturing professionals to better understand this issue and how manufacturers can better utilize this data to drive real ROI.  This infographic summarizes the results and offers our solution.

About Plutoshift: PlutoShift offers a process performance monitoring solution for a variety of process industries, including food, beverage, water and chemicals. We bring together data on one easy-to-use platform, contextualizing the information and measuring the bottom-line financial impact.

Download the report infographic here.

Download the full survey report here to read more about the challenges facing the manufacturing industry and how PlutoShift services can streamline data collection.

What we learned from hosting our first customer event

There comes a point in every B2B SaaS startup’s life when you feel the irresistible urge to host a customer event. There are many good reasons to do it. In our case, we did it because we love spending time with our potential customers and exchanging knowledge with them. We thought Austin would be a great place to host it. Tuesday, August 21st, was a hot day down there. Just perfect for a few cool drinks at the Roosevelt Room in downtown Austin and some good conversation about cowboy boots, BBQ, and Artificial Intelligence.

Plutoshift hosted this event for the Industrial team at Carollo Engineers. Their group came from all over the United States and Plutoshift had plenty to talk about. However, the topic of water was never too far away. Plutoshift’s Northern California location led to discussing wine, but eventually found it’s way to novel water reuse solutions at California vineyards. The topic of fishing somehow led to desalination plants, and skiing led to … wait for it … après-ski drinks, which led to reverse osmosis membranes in ethanol plants. Yes, the experts at Carollo care about their work.

The event, apart from giving us a chance to get to know each other, was an opportunity for the Carollo team to learn the latest in implementing machine learning and asset performance management from Plutoshift. We shared our latest work with Carollo and discussed how to take this into future projects. We touched on the advantages of a revenue-centric APM approach and also some of the challenges industrial water and wastewater companies have with implementing machine learning solutions.

Among the challenges we discussed was the lack of open source data. One thing that has put this industry behind others is the anonymous sharing of data from processes. This collaborative sharing is the key to accelerating the adoption of machine learning. Other industries, including energy, have formal programs to facilitate this type of data sharing to the betterment of the industry as a whole.

To wrap up the night, we had a frank conversation about how data sharing might be initiated. There were some good ideas that were exchanged and better still, there was enthusiasm to pursue those ideas. Perhaps the Roosevelt Room will be remembered as the launchpad for this very important component to bring revenue-centric APM approach to industrial water and wastewater plants in the future.