First things first. What is the Spatial Web?    

Technically it is a three-dimensional computing environment that can seamlessly combine layers of information gathered from countless geo-located connected devices to create seamless immersive experiences. If that sounds a lot like the Metaverse, you’re right. The two terms are often used interchangeably. However, I prefer to use the name Spatial Web instead of Metaverse, believing the former is both more accurate and descriptive.  

Whichever term you use, the best way to understand it is to experience it. The good news is you can do that right now with your smartphone!  

Head outside for a short walk using Google Maps with Live View enabled. What you will see is an Augmented Reality (AR) view of your walking path. The traditional map view will be on the bottom the screen, and the main screen area is using the camera and placing icons and directional information intelligently on top of the real-world camera view.   

This is the Spatial Web! It’s cool on the smartphone, but imagine if all sunglasses magically projected this as a layer and you did not have to hold the phone at arm’s length as you walk. This is a great way to start imagining what the Spatial Web will be in the near future as new devices seamlessly integrate into our lives. 

Ok, so why should your company care? 

The Spatial Web is coming. There’s no question about that. In fact, many of the component elements such as AR, VR, and the Internet of Things are already in use across multiple sectors. 

The gaming industry and the entertainment sector are the most visible leaders in harnessing the Spatial Web. And Facebook (now Meta) has become, perhaps, the most well-known proponent of it. 

But forward-thinking organizations in many other industries are piloting Spatial Web projects that demonstrate the expanse of potential use cases. 

There are good reasons for pursuing those use cases, too, with pilots already delivering benefits and good returns on investments. 

Let’s take an example where a complex critical system is down, say a Wind Turbine, and a mechanic working on it. The mechanic could use AR-enabled goggles to pull up instructions to guide on-site repairs. Or that mechanic could use the goggles to see design schematics that, using artificial intelligence programs, help pinpoint problems. The mechanic could also use those AR-enabled and internet-connected goggles to collaborate with an engineer from the manufacturer, with the engineer being able to see in real-time exactly what the mechanic sees and does on the machine. 

Such capabilities are already here and improving all the time, giving us a glimpse of what’s on the horizon.  

So, what’s ahead? A future where the Spatial Web will simply be part of how we live, work and engage. 

When that day arrives, these metaverse-type technologies will feel like an extension of yourself, just as smartphones have become ever-present ubiquitous tools that constantly inform, guide and connect us.  

And when that time comes, seeing someone wearing smart glasses will be the norm, not the exception. 

The timeline for that future state is years away. Gartner, a tech research firm, has predicted that widescale adoption of metaverse technologies is a decade away. 

There are, for sure, technical hurdles that need to be overcome on this Spatial Web journey. 

There have been concerns, for example, about the heat generated from the compute processing in smart glasses, the battery life in connected devices and the vertigo some suffer when using virtual reality. 

But tech companies are working on those issues, and it’s only a matter of time before they have them worked out. After all, they have the incentive to do so as there’s existing market demand for these technologies. 

And we’re already seeing tech companies deliver big advances. They are developing audio technologies to ensure immersive audio experiences. They’re maturing haptic technology, or 3D touch, so you’ll be able to actually feel those actions happening in a virtual world. Some companies are trying to do the same thing with smell. 

These technologies will work with existing ones, such as geolocational tech, sensors, artificial intelligence, 5G and eventually 6G, to instantaneously deliver layers of information to users. 

While a fully-realized Spatial Web is still years away, you shouldn’t wait to start making plans for how you will harness its potential; you can’t wait to think about your strategy until everybody starts buying connected glasses. 

If you do, then you’ll already be behind. And if you wait too long, you’ll miss out. 

The reality today, right now, is that you will have to respond to the Spatial Web as it evolves and as it delivers new ways for organizations and individuals to interact. 

This new technology-driven realm will enable increasingly frictionless services to consumers, seamless B-to-B services and new potential applications that some are already starting to imagine. 

Here at Strive, we are exploring the component technologies that collectively make up the emerging Spatial Web. 

And we’re partnering with clients to envision their Spatial Web strategies, outline the infrastructure and skills they’ll need, and devise the optimal business cases to pursue – all so they’re ready to move as the technologies mature and the Spatial Web moves into the mainstream.

Connect with Strive! 

Here at Strive Consulting, our subject matter experts’ team up with you to understand your core business needs, while taking a deeper dive into your organization’s growth strategy. Whether you’re interested in the Spatial Web or an overall Technology Enablement assessment, Strive Consulting is dedicated to being your partner, committed to success.  

Modernizing your company’s data strategy can be a daunting task. Yet making this change — and doing it right — has never been more important, with torrents of data now dictating much of the day-to-day in many organizations.

Missing the boat on making this change now can hold your business back in meaningful ways down the line. Changing your approach to capturing, sharing, and managing your data can help you avoid many of the pitfalls that befall businesses today, such as duplicating data across the organization and processing overlaps.

Implementing an effective data strategy will enable you to treat data not as an accidental byproduct of your business, but an essential component that can help you realize its full potential. Setting out clear, company-specific targets will help you tackle these challenges effectively.

Before you embark on this journey, however, it is crucial to understand why you want to modernize and where you are now and identify the most efficient path to the finish line.

Strategic Vision – Future of Your Data

The first step is to define a vision for your own data modernization. Do you know why you want to modernize your data strategy and what your business can gain in the process? Do you have an aligned strategy and a clear idea of what a thriving  Data ecosystem will entail?

Defining your goals — whether that is to gain a better grasp of your data, enhance accuracy or take specific actions based on the insights it can provide — is paramount before initializing this process.

Equally essential is to ensure early on that executive leadership is on board, since overhauling your data strategy will require significant investment in time and resources. This will be needlessly difficult without full buy-in at the very top. Figuring out how better data management will tie in with your overall business strategy will also help you make your case to leadership.

Ways of Working – Operating Model

Next, you need to figure out how this modernization will take place and pinpoint how your operating structure will change under this new and improved system.

Setting out ahead of time how data engineers and data scientists will work with managers to shepherd this strategy and maintain it in the long run will ensure a smooth process and help you avoid wasting time and resources.

Identifying what your team will look like and gathering the required resources to implement this project will lead you directly into implementation.

Accessibility & Transparency — See the Data

Gaining access and transparency, at its core, is about implementing new systems so that you gain better visibility of the data you have. You want to make sure that your structured and unstructured content — and associated metadata — is identifiable and easy to access and reference.

Putting the infrastructure in place to ingest the data your business already creates, and format it in a way that lets you access it efficiently, might appear basic. But figuring out how to achieve this through data integration or engineering is a vital step and getting this wrong can easily jeopardize the entire project.

Data Guardianship — Trust the Data

Once you have brought your data to the surface, determining ownership within your organization will ensure both that accuracy is maintained, and that data is managed and updated within the correct frameworks. 

This includes applying ethical and data sharing principles, as well as internal governance and testing, so that you can ensure your data is always up-to-date and handled responsibly. Making sure that you can trust the data you are seeing is essential to guarantee the long-time benefits you are hoping to gain through data modernization in the first place. 

Plus, you can rest easy knowing that your reporting data is accurate instead of worrying about falling foul of external compliance metrics and other publication requirements.

Data Literacy — Use the Data

Tying back to your internal data management, literacy is all about making sure that you have the right skillsets in place to make savvy use of the insights you are gaining from your data.

You and your team need to make sure you are trained and equipped to handle this process both during implementation and once your new system is in place — so you can leverage the results in the best possible way and make it easier to access and share data throughout the company.

After all, making secure financial and operational decisions will depend on how much you trust in your own core capabilities. Ideally, a successful data management strategy will enable you to understand every part of your business. This applies not just internally, but also spans your customers, suppliers and even competitors.

Take the First Step with Strive

Our experts at Strive Consulting are here to help you assess whether you are ready to embark on this journey and provide you with a clear perspective of where you are, what’s involved, and how to get there. We are ready to walk you through this process and make sure the final product ends up in the right place, so you can be confident that your data is in safe hands — your own. Learn more about Strive’s Data & Analytics and Management Consulting practices HERE.

We’ve all been in that meeting. There were probably too many people invited, the agenda is vague, 90% of folks are remote and off camera (10% of those are probably folding laundry or some other household chore while they listen in). Then you hear the inevitable words “Let’s brainstorm this.”  2 or 3 enthusiastic participants end up doing most of the talking and the conversation takes on a circular characteristic until a senior leader or manager tries to stop the swirl by making a suggestion of their own. Everyone else is inclined to fall in line, and the meeting moves on to the next arduous loop. After over an hour, you’re left wondering: What did we actually accomplish? 

It’s time to face facts that collaborating in the remote and hybrid world requires different ways of working together. The natural structures of the office that act as palaces of accountability, collaboration, and innovation have been replaced by impersonal video calls in far flung home offices.

Current trends would suggest that the reality of remote and hybrid work isn’t about to end anytime soon, but there are things you can do now to make your virtual meeting time more efficient and enjoyable.  

Here are 3 tips to start improving your virtual meetings.

• Never start from scratch – Structure and visual starting points are always in style

One thing that most people dislike is uncertainty. People are more willing to engage when they know what to expect and feel confident their time will be spent effectively. By providing agendas, objectives, and materials to review before the meeting, you’ll prime your audience or colleagues to start thinking about topics you want to discuss (even if subconsciously) and get better feedback and participation when the time comes.   

About 65% of people are visual learners ¹. Having visual aids to help provide context and bring people up to speed quickly will always supercharge your meeting efficiency, particularly in virtual settings, where you can’t draw pretty pictures on whiteboards. Having something to react to will always elicit more effective feedback and progress than trying to start from scratch

• Use design thinking techniques to unlock diversity in brainstorming sessions

Idea generation and innovation are some of the most difficult things to accomplish in a virtual setting. At Strive, we often leverage ideation techniques from design thinking practices such as affinity mapping, mind mapping, or SCAMPER (among many others) to make brainstorming more enjoyable and participatory for your team. By introducing individual brainstorming and voting principles within these techniques, you’re able to increase participation and better democratize decision making. Using these methods will help your team quickly align around creative ideas everyone can get excited about. Turning boring meetings into fun workshops helps bring some variety and intrigue to your colleague’s days – and they’ll thank you for it.

• Leverage a virtual collaboration tool like Miro^TM

Virtual white-boarding tools have made huge improvements since being thrust into the limelight during the COVID-19 pandemic. We leverage tools like Miro^TM with many of our clients to help facilitate engaging workshops, capture requirements, and build relationships. The features of the infinite virtual white-boarding canvas help you capture some of the magic previously only possible when working in person. On top of the real-time collaboration these tools enable, they also provide access to a universe of templates and ideas for creative ways to effectively facilitate a variety of types of meetings (including design thinking techniques mentioned above).

Whatever your role may be in the corporate world, meetings are practically unavoidable, but with these tips and tools, you can become the meeting hero that saves your team from boring and unproductive virtual meetings.

Strive has become experts at virtual collaboration… Need help?

Here at Strive, we take pride in our Management Consulting practice, where we can assist you in your initial digital product development needs, all the way through to completion. Our subject matter experts’ team up with you to understand your core business needs, while taking a deeper dive into your company’s growth strategy.

The enterprise data world has been captivated by a new trend: Data Mesh. The “What Is Data Mesh” articles have already come out, but in this publication, I want to highlight a live, in production, worldwide Data Mesh example – The Snowflake Data Marketplace.

As in every “new thing” that comes down the pike, people will change the definition to suit their purposes and point of view, and I am no different. Zhamak Dehghani, a Director of Emerging Technologies at ThoughtWorks, writes that Data Mesh must contain the following shifts:

• Organization: From central controlled to distributed data owners. From enterprise IT to the domain business owners.
• Technology: It shifts from technology solutions that treat data as a byproduct of running pipeline code to solutions that treat data and code that maintains it as one lively autonomous unit.
• Value: It shifts our value system from data as an asset to be collected to data as a product to serve and delight the data users (internal and external to the organization).
• Architecture: From central warehouses and data lakes to a distributed mesh of data products with a standardized interface. 

It is on this principal that I take departure and advocate the Snowflake Data Cloud. I believe that the advantages that have always been in a centralized data store can be retained, while the infinite scale of Snowflake’s Data Cloud facilitates the rest of the goals behind Data Mesh.

With so much to understand about the new paradigm and its benefits, or even grasping what an up and running Data Mesh would look like… to date, even simplified overview articles are lengthy. As I wrestled with coming to my own understanding of Data Mesh and how Strive could bring our decades of successful implementations in all things data, software development, and organizational change management to bear, I was hit by a simple notion. There is already a great example of a successfully implemented, world-wide, multi-organization Data Mesh – The Snowflake Marketplace.

There are more than 1,100 data sets from more than 240 providers, available to any Snowflake customer. The data sets from the market become part of the customer’s own Snowflake account and yet are managed and kept up to date by providers. No ETL needed and no scheduling. When providers update their data, it is updated for all subscribers. This is the definition of “data as a product”.

In effect, The Snowflake Data Cloud is the self-service, data-as-a-platform infrastructure. The Snowflake Marketplace is the discovery and governance tool within it. Everyone that has published data into the Marketplace has become product owners and delivered data as a product.

We can see the promised benefit of the Snowflake Marketplace as Data Mesh in this – massive scalability. I’m not speaking of the Snowflake platforms near infinite scalability, impressive as that is, however considering how every team publishing data into the market has been able to do so without the cooperation of another team. None of the teams that have published data have had to wait in line to have their priorities bubble up to the top of IT’s agenda.  A thousand new teams can publish data today. A hundred thousand new teams can publish their data tomorrow.

This meets the organizational shift from centralized control to decentralized domain ownership, and the data as a product, and technically with data and the code together as one product. 

Data consumers can go to market and find data that they need, regardless of which organization created the data. If it’s in the Snowflake Marketplace, any Snowflake customer can use the data for their own needs. Each consumer of the data will bring their own compute, so that nobody’s use of the data is impacting or slowing down the performance of another team’s dashboards.

Imagine that instead of weather data published by AccuWeather and financial data by Capital One – it’s your own organizations customer, employee, marketing, and logistics data. Each data set is owned by the business team that creates the data. They are the team that knows the data best. They curate, cleanse, and productize the data themselves. They do so on their own schedule and with their own resources. That data is then discoverable and usable by anyone else in the enterprise (gated by role-based security). Imagine that you can scale as your business demands, as new businesses are acquired, as ideation for new products occur. All facilitated by IT, but never hindered by IT as a bottle neck.

With Snowflake’s hyper scalability and separation of storage and compute, and its handling of structured, semi-structured, and unstructured data, it’s the perfect platform to enable enterprise IT to offer “data as self-serve infrastructure” to the business domain teams. From there, it is a small leap to see how the Snowflake Data Marketplace is, in fact, a living example of a Data Mesh with all the benefits realized in Zhamak Dehghani’s papers.

As a data practitioner with over 3 decades of my own experience, I am as excited today as ever to see the continuous evolution of how to get value out of data and deal with the explosion in data types and volumes. I welcome Data Mesh and the innovations it is promising, along with Data Vault 2.0, cloud data hyper-scale databases, like Snowflake, to facilitate the scale and speed to value of today’s data environment.

Strive is a proud partner of Snowflake!

Strive Consulting is a business and technology consulting firm, and proud partner of Snowflake, having direct experience with query usage and helping our clients understand and monopolize the benefits the Snowflake Data Platform presents. Our team of experts can work hand-in-hand with you to determine if leveraging Snowflake is right for your organization. Check out Strive’s additional Snowflake thought leadership HERE.

ABOUT SNOWFLAKE

Snowflake delivers the Data Cloud – a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Inside the Data Cloud, organizations unite their siloed data, easily discover and securely share governed data, and execute diverse analytic workloads. Join the Data Cloud at SNOWFLAKE.COM.

Executives are already sold on the benefits of moving to the cloud. They know that they need cloud computing to be agile, fast, and flexible; they know cloud allows them to successfully compete in this digital era.

Yet, many enterprise leaders struggle to advance their cloud strategies, with plenty of companies still working to migrate away from on-premise applications and out of their own data centers.

Here at Strive Consulting, we aren’t surprised by such reports: We know that cloud migration comes with numerous significant challenges…. and research backs that up.

Consider the figures from the 2022 State of the Cloud Report from the software company Flexera.

It found that understanding application dependencies is the no. 1 challenge to cloud migrations, with 53% of respondents listing this as a pain point.

Other top challenges include assessing technical feasibility, assessing on-premise vs. cloud costs, right-sizing/selecting best instance, selecting the right cloud provider, and prioritizing the applications to migrate.

Such challenges deter and derail many cloud migration plans.

Many companies don’t have the technical skills they need to address those specific challenges to move their cloud strategies forward, as their staff has, understandably, been trained and focused on supporting their on-premise and legacy systems.

On a similar note, organizations don’t have in-house workers with the experience required to analyze and assess all the available cloud options and to select the best architecture for current and future needs.

As a result, companies slow-walk – or outright put off – their cloud migrations. Or they move forward as best they can, only to realize that they need to redo their work when their new cloud infrastructure fails to yield the financial or transformational benefits they expected.

Those scenarios demonstrate why companies need an experienced hand when they migrate to the cloud and why they need people who can advise them on the right architecture for their own specific environment and their industry’s unique needs.

At Strive, we understand the myriad cloud options – from serverless, containers and virtual machines to infrastructure-as-a-service, platform-as-a-service, and software-as-a-service. We understand the nuances and requirements associated with each choice, the strategic reasons that would make one better than another, how they work together, and the supporting pieces needed to optimize each one’s performance.

Take virtual machines, for example. Going that route requires the creation of automation scripts to spin up and turn off based on use. Companies without much experience or expertise in virtual machines may overlook this critical component and, thus, end up with infrastructure that doesn’t deliver on its objectives.

Companies find that this is often the case, particularly when they’re embarking on their own.

In fact, selecting the wrong cloud option and implementing suboptimal cloud infrastructure are two of the leading reasons for poor outcomes and failed initiatives.

When we partner with companies to advance their cloud adoption, we start by understanding their own unique environment, their enterprise needs, and any industry-specific requirements that could impact their choices around cloud.

We work with our clients to determine whether, for example, they want to modernize by re-architecting their systems and using platform-as-a-service.

Whether the right move is shifting everything as is to the cloud.

Whether going with IaaS or SaaS provides the features, functions, and cost benefits they’re looking for.

Whether and when to go with hybrid, multi-cloud, multitenant, private, or public cloud.

Or whether it’s better to go the serverless route, leveraging features like containers, so they’re not paying for consumption when apps aren’t in use.

We help clients understand the financial implications of their cloud strategy decisions, and we build monitoring tools to track both performance and consumption, so they can detail what they’re using and how much that usage costs. We know from experience that finance departments are particularly interested in that information. But we also see how it benefits IT leaders, who want to allow their developers the freedom to innovate, but still want visibility into the resources being used and at what cost.

We also know from experience the importance of building a cloud environment that’s both secure and scalable, with automation in place to build that infrastructure over and over so organizations can easily build up and tear down as often as needed.

Furthermore, we advise companies on the change management that’s required to successfully migrate to the cloud. As such, we work with developers and engineers to understand new processes and to support them as they develop the expertise they’ll need to maintain, manage, and eventually mature an organizations cloud strategy.

There’s one more point I want to address: Strive knows that a cloud migration plan is not just about technology, that it’s also – and, in fact, more so – about what the technology can do for the business.

The right cloud environment enables companies to pivot quickly. Companies can rapidly and cost effectively create or adopt new functions or test and tweak proof of concepts because they can spin up and wind down computing resources.

All of this enables faster time to market with products and services and an overall more responsive organization.

Our experienced teams help clients achieve that kind of transformation by helping them design and implement the right cloud infrastructure to support those bigger objectives.

Thinking about Migrating to the Cloud? Strive can help!

We take pride in our Technology Enablement practice, where we can assist your organization with all of your cloud enablement needs. Our subject matter experts team up with you to understand your core business needs, while taking a deeper dive into Platform Assessment, Platform Migration, and even Platform Modernization.

Have you ever planned to wake up early in the morning to work out, but instead chose to lie in bed and catch up on some sleep? This can happen even after you have committed—mentally, at least—to a new workout regimen.

That’s because the hard part isn’t resolving to do something new; it’s adjusting your daily habits and generating enough momentum to carry the changes forward. This requires discipline and drive.

The same challenges apply to data governance initiatives. If you have ever been part of a data governance program that hesitated, backfired or stopped completely in its tracks, you know what I’m talking about. Companies are accruing ever-increasing amounts of data and want to be able to transform all that information into insights the same way you want to get in shape. The first step is data governance, but getting your organization to buy-in to a new program conceptually is the easy part. Taking action and sticking to it can be much more challenging.

Indeed, many organizations believe that simply implementing technology—like a Master Data Management system—will improve the health of their data. But if you simply buy workout equipment, do you get healthier? Tools will help streamline your organizational processes and complement information governance and information management, but building and maintaining a culture that treats data as an asset to your organization is the key to ongoing success.

Below are some key factors to building good habits to generate momentum once your data governance program is underway:

1. Impart a sense of urgency for the program.

For every organization with a plan to manage its data assets, there needs to be a sense of urgency to keep the plan in place. The reasons are unique from organization to organization, but they might be driven by compliance, customer satisfaction, sales, revenues, or M&A. Regardless of the reason, it needs to resonate with senior leadership and ideally be tied to the company’s strategic goals in order to be most effective.

2. Communicate, communicate, communicate.

The cornerstone to a successful data governance program is a well-organized (cross-departmental) communication plan. A solid plan helps remove the silos and maintain cross departmental support for the initiative. Seek your champions throughout the organization and meet with key stakeholders regularly to document their pain points. It is important to get people engaged early to keep the excitement going.

3. Operationalize change within the organization.

Your delivery will need to be agile in nature because the plan you put in place will naturally evolve. The goal is to learn what works within your organization early on to ensure you deliver value quickly and the process is sustainable moving forward. Complete tasks iteratively and agree upon a small set of high-value data attributes to aid in validating your data governance process. In addition, manage your data elements to ensure their best quality.

4. Make the plan as RACI as possible.

Actively listen to your supporters and put together a plan that encompasses a RACI (Responsible, Accountable, Consulted & Informed) model so that everyone on the team knows their role across the process. This plan will keep your team focused and guide your initiatives moving forward. You’ll raise your odds of success by forming a strong governance organizational structure with roles and responsibilities in place (for data ownership, stewardship and data champions), along with approvals that complement your existing change management process.

4. Measure, Communicate, Repeat.

Keep in mind that “you can’t manage what you don’t measure.” You’ll need to face the facts and communicate your findings. It’s wise to document and implement KPIs (Key Performance Indictors) so that you can measure the progress of your initiative over time. Linking the KPIs to revenue or sales loss, for example, can be a strong indicator to help drive change. As you learn more about your data, it’s important to communicate what’s meaningful to your stakeholders and continue to move forward.

Similar to continuing on a workout regimen, data governance demands a discipline that takes time and patience to fine tune. This requires changing years of undisciplined behaviors regarding data within your organization, and the change will not happen overnight. Changing these behaviors is an ongoing process that needs to resonate throughout an organization’s culture in order for success to occur.

In addition, it’s important to keep things fresh. When working out, you need to rotate though different core muscle groups and vary the routine to keep things interesting and progressive.  It’s the same with data governance initiatives. Don’t let people get bored with the same repetitive activities day in and day out. Try conducting data discovery sessions where team members present findings from an internal or external dataset that would be interesting to other team members. You can also share successes and learnings from past data related projects to drive discussion.  Another suggestion is to discuss future cross-departmental data projects (or “wish list” items) that can lead into great data roadmap discussions.  The objective is to keep everyone engaged and finding value in meetings so that the team continues to show up and make progress.

Remember that data governance is a journey that requires commitment and hard work. As with exercise, just working out for a month is a great start, but it’s with continued dedication that you really start to notice the change. If you want to take your organization to the next level, you need to develop the discipline toward information management that your organization requires for long-term sustainable success. For those with little experience in implementing or maintaining a data governance plan, experienced consultants can be of great value.

Strive Can Help With Your Data Governance Needs! 

Here at Strive Consulting, our subject matter experts’ team up with you to understand your core business needs, while taking a deeper dive into your organization’s growth strategy. Whether you’re interested in modern data integration or an overall data and analytics assessment, Strive Consulting is dedicated to being your partner, committed to success. Learn more about our Data & Analytics practice HERE.

Most of us know Design Language Systems (DLS) as a collection of tech used to build apps for Android, iOS, or Salesforce. It’s the catalog of components where we get the iOS drawer, android buttons, salesforce cards, etc. However, too few of us have thought about how we can create engaging and efficient DLS for our clients. Tackling problems like the evolution of a catalog of components to a DLS that has efficiencies built in could speed up design process, cut down on redundancies, and provide value impact to organizations and designers alike.

In addition to creating DLS components, designers should also look into incorporating Figma’s Auto Layout components as an easy way to maintain alignment as components change size. Building Auto Layout into DLS cuts production time in half and yields high-fidelity mockups. Strive’s Technology Enablement experts and team of UX/UI designers come in doing just that, spending less time on production work and more time problem solving. Remember, the more time designers spend designing mockups, the more money and resources companies are paying.

Greater speed also means having the ability to quickly adjust to new requirements or iterating based on feedback. These changes are usually setbacks, not just for the designer, but for developers and other downstream teammates that depend on the mockups.

Lets take a look at how DLS + Auto Layout can speed up your design process:

How many times have you had to take out a component and then detach it, resize it, and then measure it to make sure it fits the grid? For those that don’t have a DLS at all, it’s even more nuanced. So, instead of doing either, we simply click on the text ‘label’ and type in the new label, ‘addresses’. Thanks to Auto Layout, the component resizes to fit the bigger text.

What if we want to change the state to make it look like ‘addresses’ has been selected? Well, with a set of prebuilt components, we can easily swap the unhighlighted, default navigation bar with the highlighted version. Note that even with swapping the components, the Auto Layout ensures that the text, padding, position, styling etc. are kept intact.

The next four GIF’s are an example of true time saving potential and how Strive partners with our clients to provide value.

If you work with data-heavy applications, you’re used to the extraneous work that revolves around creating or updating tables. Generally, this means resizing, measuring, and aligning every column, just to update a single table! Not to mention dealing with tables that are particularly long and take hours of your time. Instead, Strive has developed a component that allows designers to simply bring a table component out, edit a column to fit the styling using switches, and then copy & paste to create a table. In some of our even more advanced tables, we have developed columns where placeholders like currency, data, etc. has been prefilled.

It took us less than a minute to create… Can your DLS do that?

Interested in learning more?

Here at Strive, we take pride in our Technology Enablement practice, where we can assist you in your UX/UI needs. Our subject matter experts team up with you to understand your core business needs, while taking a deeper dive into your organization’s growth strategy. Click to learn more about our digital experience capabilities and how we can help.

Authored by Strive Technology Enablement Practice

Strategic initiative failure rates remain high but working with the right partner can yield success. As such, executives put tremendous resources into planning and implementing their transformative projects.

However, seasoned executives also know that the success of those projects rests on getting users to adapt to new technologies, new processes, and new ways of working as much as – if not even more so – than any other element of the endeavor.

Unfortunately, successful change remains elusive. The failure rate for all change initiatives has been stuck around 70% for the past two decades and remains there today. ¹

Consider figures from Gartner, the tech advisory firm: Its research shows that only 34% of all organizational change initiatives are a clear success, while half are out-and-out failures. ²

Those figures tell only part of the story, though. Here at Strive Consulting, we’ve found that companies without internal Change Management teams generally experience even higher failure rates. Why? Because they have neither the deep knowledge, nor the experience and tools, to enable change.

As a result, these companies often use online tutorials that offer only highlights on the topic, or they rely on overly complex white papers that don’t provide guidance on tailoring a program to the organizations’ own unique needs.

Neither option delivers information on the concrete tools and techniques needed to effectively teach people how to work in new and different ways. Rather, they tend to focus on the psychology – how the end user feels about the changes – and share some generic guiding principles, such as the ‘importance of communication’.

In reality, Change Management is a specialized skill, and it is one that needs to be expertly adapted to each initiative and tailored to every organization to ensure success. Strive’s Change Management framework acknowledges that reality and brings together four critical elements that must be addressed for an organization to successfully navigate transformation.

Those four elements are:

• Alignment and Engagement
• Change Impact and Analytics
• Communication
• Readiness and Training

Our extensive experience in helping a broad range of clients steer their companies through change has allowed us to hone in on these key areas and build a Change Management framework that leverages each of them to the maximum effect. We’ll focus on five critical tools across three elements of our framework

Let’s look at the first element: Alignment and Engagement. This element ensures that we’re collaborating with the right people in the plan and that their goals and priorities are well understood. With our ‘Story for Change’ we ask five important questions: What is happening, why now, so what, how are we going to achieve this, and now what? Asking these questions and listening to responses from project leaders gives us and, more importantly, the organization a clear, precise understanding on where it wants to be at the end of the transformation. While collaborating with these same leaders, we group and assess different stakeholder cohorts on a 2×2 grid measuring one’s level of influence on success and one’s impact imposed. The Stakeholder Assessment is the backbone to tailoring change, considering that all cohorts are coming from very different starting points and have different roles within the broader future state.

Next, we’re looking at Change Impacts and Analytics. For this, Strive evaluates how someone’s responsibilities will change and by how much. With Change Analysis we document all unique impacts and map against the stakeholder cohorts, identifying whether groups will perceive the impact as positive, negative, or neutral. This lets us understand what users will feel about the changes they’re facing and develop the various engagement, communication, and training activities needed to build understanding, knowledge, and commitment. We also develop metrics that track adoption, so we can confirm success, as well as identify those cohorts who may need additional support.

In tandem, we’re planning necessary Communications. This is all about informing key stakeholders through integrated, targeted, and timely program messaging. It’s also about understanding how communication flows within an organization. We believe there must be a communication cascade strategy within any program undergoing change for it to successfully transform. So, top-level sponsors need to effectively communicate with their direct reports, and in turn those managers need to effectively convey messages to their teams. Moreover, a communication plan compliments this cascade of information for each audience. Communication timed appropriately, focused on the right message, and delivered via the right vehicle helps all parties understand the importance of transformation for the organization as a whole.

On top of all this, we evaluate Readiness and Training. While training is hyper-focused and can be niche, we’ll focus on readiness. Quantitative metrics showing before and after results tell a clear part of the story, but it is one-sided. Qualitative surveying helps leadership understand if, and by how much, do stakeholder cohorts and users understand why the change is taking place, are aware of the impacts to their day-to-day responsibilities, know where they go for resources, and believe the change is overall positive.

Now, none of these four framework elements works in isolation. Rather, we consider them all together. In fact, we factor them into the lifecycle of a broader Change Management approach, creating a timeline from start to go-live that includes markers along the way. This means planning, for example, what milestones should be achieved counting down from 90, 60, 30, 15, 7, and 1 day out.

The payoff for having a structured Change Management workstream is significant, with This alone shows the value of having a solid Change Management strategy in place and the importance of having a partner who can deliver such results.

Looking for sample deliverables? Or maybe a bit more information? Let’s Talk!  

Here at Strive, we take pride in our Management Consulting practice, where we can assist you in your initial digital product development needs, all the way through to completion. Our subject matter experts’ team up with you to understand your core business needs, while taking a deeper dive into your company’s growth strategy.

“Have your cake and eat it too.” How would it make sense to have cake and not be able to eat it? And yet, we have, for decades, had similar experiences with enterprise data warehouses. We have our data; we want to query it too!

Organizations spend so much time, effort, and resources building a single source of truth. Millions of dollars are spent on hardware and software and then there is the cleansing, collating, aggregating, and applying business rules to data. When it comes time to query… we pull data out of enterprise data warehouse and put it into data marts. There simply is never enough power to service everybody who wants to query the data.

With the Snowflake Data Cloud, companies of all sizes can store their data in one place – and every department, every team, every individual can query that data. No more need for the time, expense, effort, and delay to move data out of an enterprise data warehouse and into data marts.

The advance of the ‘data lake’ promised to be the place where all enterprise data could be stored. Structured, semi-structured, and unstructured data could be stored together, cost effectively. And yet, as so many soon found out – data ended up needing to be moved out to achieve the query performance desired. More data marts, more cost, and more time delay to get to business insight.

Snowflake solved this problem by separating data storage from compute. Departments and teams can have their own virtual warehouse, a separate query compute engine that can be sized appropriately for each use case. These query engines do not interfere with each other.  Your data science team can run massive and complex queries without impacting accounting team’s dashboards.

Snowflake does this having designed for the cloud from the ground up. A massively parallel processing database, Snowflake is designed to use the cloud infrastructure and services of AWS, quickly followed by Azure and GCP. Organizations get all the scalability promised by “Hadoop based Big Data” in an easy to use, ANSI Standard SQL data warehouse, that delivers the 5 V’s of big data (Volume, Value, Variety, Velocity and Veracity). Not to mention all of these benefits come with industry leading cost and value propositions.

Speaking of Variety… Snowflake has broken out of the “data warehouse” box and has become ‘The Data Cloud’. All your data types: structured, semi-structured and now, unstructured.  All your workloads: Data Warehouse, Data Engineering, Data Science, Data Lake, Data Applications, and Data Marketplace. You have the scalability in data volume and in query compute engines across all types of data and use cases.

With the Snowflake Data Cloud, you truly can have all your data and query it too. Extracting business value for all departments and all employees along the way.

Want to learn more about the Snowflake Data Cloud? 

Strive Consulting is a business and technology consulting firm, and proud partner of Snowflake, having direct experience helping our clients understand and monopolize the benefits the Snowflake Data Platform presents. Our team of experts can work hand-in-hand with you to determine if leveraging Snowflake is right for your organization. Check out Strive’s additional Snowflake thought leadership HERE.

About Snowflake

Snowflake delivers the Data Cloud – a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Inside the Data Cloud, organizations unite their siloed data, easily discover and securely share governed data, and execute diverse analytic workloads. Join the Data Cloud at SNOWFLAKE.COM.

In 2022, almost every enterprise has some cloud footprint, especially around their data. These cloud platforms offer closed-source tools which, while offering many benefits, may not always be the best choice for some organizations. First and foremost, these proprietary services can be expensive. In addition to paying for the storage and compute needed to store and access data, you also end up paying for the software itself. You could also become locked into a multi-year contract, or you might find yourself locked into a cloud’s tech stack. Once that happens, it’s very difficult (and expensive) to migrate to a different technology or re-tool your tech stack. To put it simply, if you ever reach a roadblock your closed-source tool can’t solve, there may be no workarounds.

Since closed-source technologies can create a whole host of issues, open-source technologies may be the right choice. Open-source tech is not owned by anyone. Rather, anyone can use, repackage, and distribute the technology. Several companies have monetized open-source technology by packaging and distributing it in innovative ways. Databricks, for example, built a platform on Apache Spark, a big-data processing framework. In addition to providing Spark as a managed service, Databricks offers a lot of other features that organizations find valuable. However, a small organization might not have the capital or the use case that a managed service like Databricks aims to solve. Instead, you can deploy Apache Spark on your own server or a cloud compute instance and have total control. This is especially attractive when addressing security concerns. An organization can benefit from a tool like Spark without having to involve a third party and risk exposing data to the third party.

Another benefit is fine-tuning resource provisioning.

Because you’re deploying the code on your own server or compute instance, you can configure the specifications however you want. That way, you can avoid over-provisioning or under-provisioning. You can even manage scaling, failover, redundancy, security, and more. While many managed platforms offer auto-scaling and failover, it is never so granular as it is when you provision resources yourself.

Many proprietary tools, specifically ETL (Extract, Transfer, Load) and data integration tools, are no-code GUI based solutions that require some prior experience to be implemented correctly. While the GUIs are intended to make it easier for analysts and less-technical people to create data solutions, more technical engineers can find it frustrating. Unfortunately, as the market becomes more inundated with new tools, it can be difficult to find proper training and resources. Even documentation can be iffy! Open-source technologies can be similarly peculiar, but it’s entirely possible to create an entire data stack – data engineering, modeling, analytics, and more – all using popular open-source tech. These tools will almost certainly lack a no-code GUI but are compatible with your favorite programming languages. Spark supports Scala, Python, Java, SQL and R, so anyone who knows one of those skills can be effective using Spark.

But how does this work with cloud environments?

You can choose how much of the open-source stack you want to incorporate. A fully open-source stack would simply be running all your open-source data components on cloud compute instances: database, data lake, ETL, data warehouse, and analytics all on virtual machine(s). However, that’s quite a bit of infrastructure to set up, so it may make sense to unload some parts to cloud-native technologies. Instead of creating and maintaining your own data lake, it would make sense to use AWS S3, Azure Data Lake Storage gen2, or Google Cloud Storage. Instead of managing a compute instance for a database, it would make sense to use AWS RDS, Azure SQL DB, or Google Cloud SQL and use an open-source flavor of database like MySQL or MariaDB. Instead of managing a Spark cluster, it might make sense to let the cloud manage the scaling, software patching, and other maintenance, and use AWS EMR, Azure HDInsight, or Google Dataproc. You could also abandon the idea of using compute instances and architect a solution using a cloud’s managed open-source offerings: AWS EMR, AWS MWAA, AWS RDS, Azure Database, Azure HDInsight, GCP’s Dataproc and Cloud Composer, and those are just data-specific services. As mentioned before, these native services bear some responsibility for maintaining the compute/storage, software version, runtimes, and failover. As a result, the managed offering will be more expensive than doing it yourself, but you’re still not paying for software licensing costs.

In the end, there’s a tradeoff.

There’s a tradeoff between having total control and ease of use, maintenance, and cost optimization, but there is a myriad of options for building an open-source data. You have the flexibility to host it on-premises or in the cloud of your choice. Most importantly, you can reduce spend significantly by avoiding software licensing costs.

Interested in Learning More About Open-Source Technologies? 

Here at Strive Consulting, our subject matter experts’ team up with you to understand your core business needs, while taking a deeper dive into your organization’s growth strategy. Whether you’re interested in modern data integration or an overall data and analytics assessment, Strive Consulting is dedicated to being your partner, committed to success. Learn more about our Data & Analytics practice HERE.