By now, you’ve likely heard a lot about Big Data. You may have even heard about “the three Vs” of Big Data. Originally defined by Gartner, “Big Data is “high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision-making, insight discovery and process optimization.”
Digital transformation is ramping up in all industries. Facing regular market disruptions, and landscape-changing technological breakthroughs, modern businesses must be both malleable and willing to change.
To stay competitive, you must be agile.
Increasing numbers of organizations are undergoing a digital transformation. The tried-and-tested yet rigid methods of doing business are being replaced by newer, data-orientated approaches that require thorough but fast analysis.
Some businesses – like Amazon, Netflix and Uber – are leading this evolution. They all provide very different services, but at their core, they are technology focused.
And they’re reaping rewards for it too. Amazon is one of the most valuable businesses in the world, perhaps one of the first companies to reach a $1-trillion valuation.
It’s not too late to adopt digital transformation, but it is too late to keep fighting against it. The tide of change has quickened, and stubborn businesses could be washed away.
But what’s the best way to get started?
Any form of change must start with the end in mind, as it’s impossible to make a transformation without understanding why and how.
Before you make a change, big or small, you need to ask yourself why are we doing this? What are the positives and negatives? And if there are negatives, what can we do to mitigate them?
To ensure a successful digital transformation, it’s important to plot your journey from the beginning through your end goal, understanding how one change or a whole series of changes will alter your business.
Business process modeling tools can help map your digital transformation journey.
For businesses of any size, transformational change can disrupt day-to-day operations. In most organizations, the expertise to manage a sizeable transformation program doesn’t exist, and from the outset, it can appear quite daunting.
If your goal is to increase profits, it can seem contradictory to pay for support to drive your business forward. However, a slow or incorrect transformational process can be costly in many ways. Therefore, investing in support can be one of the best decisions you make.
Effective strategic planning, rooted in enterprise architecture, can help identify gaps and potential oversights in your strategy. It can indicate where investment is needed and ensure transformative endeavors aren’t undermined by false-starts and U-turns.
Many businesses would benefit further by employing strategic consultants. As experts in their fields, strategic consultants know the right questions to ask to uncover the information you need to influence change.
Their experience can support your efforts by identifying and cataloging underlying components, providing input to the project plan and building the right systems to capture important data needed to meet the business’s transformation goals.
Once you know where you want to go, it’s important to understand what you currently do. That might seem clear, but even the smallest organizations are underpinned by thousands of business processes.
Before you decide to change something, you need to understand everything about what you currently do, or else a change could have an unanticipated and negative impact.
Enterprise architecture will also benefit a business here, uncovering strategic improvement opportunities – valuable changes you might not have seen.
As third-parties, consultants can provide an impartial view, rather than letting historic or legacy decisions cloud future judgment.
Businesses will also benefit from data modeling. This is due to the exponential increase in the volume of data businesses have to manage – as well as the variety of disparate sources.
Data modeling will ensure data is accessible, understood and better prepared for analysis and the decision-making process.
Your employees are a wealth of knowledge and ideas, so it’s important to involve them in the enterprise architecture process.
Consultants can facilitate a series of staff workshops to enable employee insights to be shared and then developed into real, actionable changes.
Once you’ve engaged with your staff to collect the knowledge they hold, make sure you don’t cut them off there. Business change is only successful if everyone understands what is happening and why, with continuous updates.
Ensure that you take your employees through the change process, making them part of the digital transformation journey.
Evidence suggests that 70 percent of all organizational change efforts fail, with a primary reason being that executives don’t get enough buy-in for new initiatives and ideas.
By involving relevant stakeholders in the strategic planning process, you can mitigate this risk. Strategic planning tools that enable collaboration can achieve this. Thanks to technological advancements in the cloud, collaboration can even be effectively facilitated online.
Take your employees through your digital transformation journey, and you’ll find them celebrating with you when you arrive at your goal.
If you think now is the right time for your business to change, get in touch with us today.
Customer journey architects are becoming more relevant than ever before.
For businesses that want to make improvements, enterprise architecture has long been a tried and tested technique for mapping out how change should take place.
Despite the nomenclature, enterprise architecture, data architecture and business process architecture are very different disciplines. Despite this, organizations that combine the disciplines enjoy much greater success in data management.
Both an understanding of the differences between the three and an understanding of how the three work together, has to start with understanding the disciplines individually:
Enterprise architecture defines the structure and operation of an organization. Its desired outcome is to determine current and future objectives and translate those goals into a blueprint of IT capabilities.
A useful analogy for understanding enterprise architecture is city planning. A city planner devises the blueprint for how a city will come together, and how it will be interacted with. They need to be cognizant of regulations (zoning laws) and understand the current state of city and its infrastructure.
A good city planner means less false starts, less waste and a faster, more efficient carrying out of the project.
In this respect, a good enterprise architect is a lot like a good city planner.
The Data Management Body of Knowledge (DMBOK), define data architecture as “specifications used to describe existing state, define data requirements, guide data integration, and control data assets as put forth in a data strategy.”
So data architecture involves models, policy rules or standards that govern what data is collected and how it is stored, arranged, integrated and used within an organization and its various systems. The desired outcome is enabling stakeholders to see business-critical information regardless of its source and relate to it from their unique perspectives.
There is some crossover between enterprise and data architecture. This is because data architecture is inherently an offshoot of enterprise architecture. Where enterprise architects take a holistic, enterprise-wide view in their duties, data architects tasks are much more refined, and focussed. If an enterprise architect is the city planner, then a data architect is an infrastructure specialist – think plumbers, electricians etc.
For a more in depth look into enterprise architecture vs data architecture, see: The Difference Between Data Architecture and Enterprise Architecture
Business process architecture describes an organization’s business model, strategy, goals and performance metrics.
It provides organizations with a method of representing the elements of their business and how they interact with the aim of aligning people, processes, data, technologies and applications to meet organizational objectives. With it, organizations can paint a real-world picture of how they function, including opportunities to create, improve, harmonize or eliminate processes to improve overall performance and profitability.
A successful data-driven business combines enterprise architecture, data architecture and business process architecture. Integrating these disciplines from the ground up ensures a solid digital foundation on which to build. A strong foundation is necessary because of the amount of data businesses already have to manage. In the last two years, more data has been created than in all of humanity’s history.
And it’s still soaring. Analysts predict that by 2020, we’ll create about 1.7 megabytes of new information every second for every human being on the planet.
While it’s a lot to manage, the potential gains of becoming a data-driven enterprise are too high to ignore. Fortune 1000 companies could potentially net an additional $65 million in income with access to just 10 percent more of their data.
To effectively employ enterprise architecture, data architecture and business process architecture, it’s important to know the differences in how they operate and their desired business outcomes.
Historically, these three disciplines have been siloed, without an inherent means of sharing information. Therefore, collaboration between the tools and relevant stakeholders has been difficult.
To truly power a data-driven business, removing these silos is paramount, so as not to limit the potential analysis your organization can carry out. Businesses that understand and adopt this approach will benefit from much better data management when it comes to the ‘3 Vs.’
They’ll be better able to cope with the massive volumes of data a data-driven business will introduce; be better equipped to handle increased velocity of data, processing data accurately and quickly in order to keep time to markets low; and be able to effectively manage data from a growing variety of different sources.
In essence, enabling collaboration between enterprise architecture, data architecture and business process architecture helps an organization manage “any data, anywhere” – or Any2. This all-encompassing view provides the potential for deeper data analysis.
However, attempting to manage all your data without all the necessary tools is like trying to read a book without all the chapters. And trying to manage data with a host of uncollaborative, disparate tools is like trying to read a story with chapters from different books. Clearly neither approach is ideal.
Unifying the disciplines as the foundation for data management provides organizations with the whole ‘data story.’
The importance of getting the whole data story should be very clear considering the aforementioned statistic – Fortune 1000 companies could potentially net an additional $65 million in income with access to just 10 percent more of their data.
Download our eBook, Solving the Enterprise Data Dilemma to learn more about data management tools, particularly enterprise architecture, data architecture and business process architecture, working in tandem.
Data-driven business is booming. The dominant, driving force in business has arguably become a driving force in our daily lives for consumers and corporations alike.
We now live in an age in which data is a more valuable resource than oil, and five of the world’s most valuable companies – Alphabet/Google, Amazon, Apple, Facebook and Microsoft – all deal in data.
However, just acknowledging data’s value won’t do. For a business to truly benefit from its information, a change in perspective is also required. With an additional $65 million in net income available to Fortune 1000 companies that make use of just 10 percent more of their data, the stakes are too high to ignore.
Traditionally, data management only concerned data professionals. However, mass digital transformation, with data as the foundation, puts this traditional approach at odds with current market needs. Siloing data with data professionals undermines the opportunity to apply data to improve overall business performance.
The precedent is there. Some of the most disruptive businesses of the last decade have doubled down on the data-driven approach, reaping huge rewards for it.
Airbnb, Netflix and Uber have used data to transform everything, including how they make decisions, invent new products or services, and improve processes to add to both their top and bottom lines. And they have shaken their respective markets to their cores.
Even with very different offerings, all three of these businesses identify under the technology banner – that’s telling.
One key reason for the success of data-driven business, is the alignment of common C-suite goals with the outcomes of a data initiative.
Those goals being:
This list of C-suite goals is, in essence, identical to the business outcomes of a data-driven business strategy.
In the early stages of data transformation, businesses tend to take an ad-hoc approach to data management. Although that might be viable in the beginning, a holistic data-driven strategy requires more than makeshift efforts, and repurposed Office tools .
Organizations that truly embrace data, becoming fundamentally data-driven businesses, will have to manage data from numerous and disparate sources (variety) in increasingly large quantities (volume) and at demandingly high speeds (velocity).
To manage these three Vs of data effectively, your business needs to take an “any-squared” (Any2) approach. That’s “any data” from “anywhere.”
By leveraging a data management platform with data modeling, enterprise architecture and business process modelling, you can ensure your organization is prepared to undergo a successful digital transformation.
Data modeling identifies what data you have (internal and external), enterprise architecture determines how best to use that data to drive value, and business process modeling provides understanding in how the data should be used to drive business strategy and objectives.
Therefore, the application of the above disciplines and associated tools goes a long way in achieving the common goals of C-suite executives.
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In light of data’s prominence in modern business, organizations need to ensure they have a strong data foundation in place.
The ascent of data’s value has been as steep as it is staggering. In 2016, it was suggested that more data would be created in 2017 than in the previous 5000 years of humanity.
But what’s even more shocking is that the peak still not may not even be in sight.
To put its value into context, the five most valuable businesses in the world all deal in data (Alphabet/Google, Amazon, Apple, Facebook and Microsoft). It’s even overtaken oil as the world’s most valuable resource.
Yet, even with data’s value being as high as it is, there’s still a long way to go. Many businesses are still getting to grips with data storage, management and analysis.
Fortune 1000 companies, for example, could earn another $65 million in net income, with access to just 10 percent more of their data (from Data-Driven Business Transformation 2017).
We’re already witnessing the beginnings of this increased potential across various industries. Data-driven businesses such as Airbnb, Uber and Netflix are all dominating, disrupting and revolutionizing their respective sectors.
Interestingly, although they provide very different services for the consumer, the organizations themselves all identify as data companies. This simple change in perception and outlook stresses the importance of data to their business models. For them, data analysis isn’t just an arm of the business… It’s the core.
The dominating data-driven businesses use data to influence almost everything. How decisions are made, how processes could be improved, and where the business should focus its innovation efforts.
However, simply establishing that your business could (and should) be getting more out of data, doesn’t necessarily mean you’re ready to reap the rewards.
In fact, a pre-emptive dive into a data strategy could in fact, slow your digital transformation efforts down. Hurried software investments in response to disruption can lead to teething problems in your strategy’s adoption, and shelfware, wasting time and money.
Additionally, oversights in the strategy’s implementation will stifle the very potential effectiveness you’re hoping to benefit from.
Therefore, when deciding to bolster your data efforts, a great place to start is to consider the ‘three Vs’.
The three Vs of data are volume, variety and velocity. Volume references the amount of data; variety, its different sources; and velocity, the speed in which it must be processed.
When you’re ready to start focusing on the business outcomes that you hope data will provide, you can also stretch those three Vs, to five. The five Vs include the aforementioned, and also acknowledge veracity (confidence in the data’s accuracy) and value, but for now we’ll stick to three.
As discussed, the total amount of data in the world is staggering. But the total data available to any one business can be huge in its own right (depending on the extent of your data strategy).
Unsurprisingly, vast volumes of data are sourced from a vast amount of potential sources. It takes dedicated tools to be processed. Even then, the sources are often disparate, and very unlikely to offer worthwhile insight in a vacuum.
This is why it’s so important to have an assured data foundation upon which to build a data platform on.
The Any2 approach is a strategy for housing, sorting and analysing data that aims to be that very foundation on which you build your data strategy.
Shorthand for Any Data, Anywhere, Any2 can help clean up the disparate noise, and let businesses drill down on, and effectively analyze the data in order to yield more reliable and informative results.
It’s especially important today, as data sources are becoming increasingly unstructured, and so more difficult to manage.
Big data for example, can consist of click stream data, Internet of Things data, machine data and social media data. The sources need to be rationalized and correlated so they can be analyzed more effectively.
When it comes to actioning an Any2 approach, a fluid relationship between the various data initiative involved is essential. Those being, Data Modeling, Enterprise Architecture, Business Process, and Data Governance.
It also requires collaboration, both in between the aforementioned initiatives, and with the wider business to ensure everybody is working towards the same goal.
With a solid data foundation platform in place, your business can really begin to start realizing data’s potential for itself. You also ensure you’re not left behind as new disruptors enter the market, and your competition continues to evolve.
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Business process management’s role in utilizing knowledge is, in essence, about alignment, making sure you have the key pieces of knowledge from individual employees, departments and operations. This way, businesses can make better decisions with greater context, based on the full picture.
The early stages of adopting a data strategy often involve an ad-hoc approach to data management. Rather than invest in a suite of new tools, businesses tend to make do with what they have already, starting small and eventually formalizing the approach.
In many cases, this could be the best approach – or at least better than wasting an investment on shelfware, right?
But if your business wants to use its data effectively, a point inevitably will come when the data has outgrown the makeshift means in which it is managed.
Enterprise architecture (EA), for example, can start as a collection of Visio files, Excel spreadsheets and PowerPoint slides, but it’s never long before the EA starts spilling from the Office Tools and onto the desk – literally. It’s not uncommon to see EA represented as Post-it Notes haphazardly scattered across a workstation.
For a while, an enterprise architect might be able to maintain this approach. But when the information needs to be shared with the wider business to influence decisions and strategy, the lack of structure can make the findings difficult to comprehend.
This not only slows down time to markets and leads to inaccurate analysis and results, but it also undermines EA’s value in the eyes of stakeholders and decision-makers. If they can’t clearly see the business outcomes, then why bother investing more money in the discipline?
Taking this approach also limits the potential analysis an organization can even carry out. With the Office Tools approach, even though the software all falls under the “Office” bracket, the files and systems are still disparate.
Traditionally, this was less of an issue for EA in years gone by. Foundational EA, as we refer to it today, was and is about support, rather than innovation. Businesses weren’t really actioning an EA initiative to give insight into where they could innovate, the likelihood of disruption, and how that disruption could be capitalized on.
EA was more about “legacy” IT tasks like keeping the lights on, highlighting redundant systems and processes, and trimming fat to keep costs low. In other words, it was more concerned about the current state of the business and less about what needs to be done to achieve the desired future state.
In-depth and all-inclusive analysis required to maximize the data’s potential benefits, needs to be stored in one repository.
And what happens if your enterprise architect leaves? His/her work may be rendered useless to the business due to the lack of formality.
So don’t fool yourself about data management. Given all these consideration, you need to invest in effective data management so your business can truly capitalize on its data and the valuable insights it will provide.
In the modern world, data education is immensely important.
Data has become a fundamental part of how businesses operate. It’s also essential to consumers in going about their day-to-day lives.
And while organizations and consumers alike go about their business, data constantly ticks in the background, enabling the systems and processes that keep the world functioning.
Considering this, and with March marking Data Education Month, now seems the perfect time to highlight the importance of understanding data’s potential, its drawbacks and the most efficient ways to ensure its effective management.
In 2013, the total amount of data in the world was believed to have reached 4.4 zettabytes. For context, 1 zettabyte is equivalent to around 44 trillion gigabytes, or about 152 million years of UHD 8K video format.
By 2020, analysts predict the world’s data will reach 44 zettabytes. The sudden acceleration is truly staggering, and it’s businesses driving it.
Start-ups that find new ways to exploit data can revolutionize markets almost overnight. And as the frequency in which this happens increases, more and more pre-established businesses are also putting resources behind digital innovation.
By now, businesses should be more than aware of just how important a good data management strategy is. If you’ve yet to make a data strategy a central focus of the way your business operates, then chances are, you’re being left behind – and the gap is widening quickly.
So in honor of data education month, we’ve collated some of our top educational data posts, and a few others around the Web.
Read, comment, share and celebrate #DataEducationMonth with us.
Managing Any Data, Anywhere with Any²
The acceleration in the amount of data is staggering, and can be overwhelming for businesses. You should apply the Any² approach to cope.
GDPR Guide: Preparing for the Changes
Businesses need to prepare for changes to General Data Protection Regulation (GDPR) legislation, and our GDPR guide is a great place to start.
Using EA, BP and DM to Build the Data Foundation Platform
Instead of utilizing built for purpose data management tools, businesses in the early stages of a data strategy often leverage pre-existing, make-shift software. However, the rate in which modern businesses create and store data, means these methods can be quickly outgrown.
The Data Vault Method for Modeling the Data Warehouse
How the data vault method benefits businesses by improving implementation times, and enabling data warehouse automation.
Data Modeling – What the Experts Think
Three data modeling experts share their advice, opinions and best practices for data modeling and data management strategies.
Data-Driven Enterprise Architecture for Better Business Outcomes
A business outcome approach to enterprise architecture can reduce times to market, improve agility, and make the value of EA more apparent.
What’s Behind a Successful Enterprise Architecture Strategy?
Best practices to adopt to increase the likelihood of enterprise architecture’s success
Basics of Business Process Modeling
Business process modeling helps to standardize your processes and the ways in which people communicate, as well as to improve knowledge sharing.
Where Do I Start with Business Process Modeling?
FAQ blog providing insight from top consultants into key issues impacting the business process and enterprise architecture industries.
Over the past few weeks we’ve been exploring aspects related to the new EU data protection law (GDPR) which will come into effect in 2018.