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.
‘Knowledge is power’ – a well-known phrase and one that is especially true in the business world. Statistics show that Fortune 500 companies lose $31.5 billion each year by failing to gather and share knowledge effectively. So knowing the best way to undertake every business process you have will help drive your business forward.
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.
The amount of data in the world is staggering. And as more and more organizations adopt digitally orientated business strategies the total keeps climbing. Modern organizations need to be equipped to manage Any2 – any data, anywhere.
Analysts predict that the total amount of data in the world will reach 44 zettabytes by 2020 – one zettabyte = 44 trillion gigabytes. That’s an incredible feat in and of itself. But considering the fact that the total had only reached 4.4 zettabytes in 2013, the rate at which data is collected and stored becomes even more astonishing.
However, it is equally incredible that less than 0.5% of that data is currently analyzed and/or utilized effectively by the business.
Perhaps the most obvious answer is opportunity. You likely wouldn’t be reading this blog if you weren’t at least passively aware of the potential insight that can be derived from a series of ones and zeros.
Start-ups such as Uber, Netflix and Airbnb are perhaps some of the best examples of data’s potential being realized. It’s even more apparent when you consider these three organizations refer to themselves as technology companies, as opposed to the fields their services fall under.
But with data’s potential, potentially open for any business to invest in, action, and benefit from, competition is more fierce than ever, which brings us to what else this new wave of data means for business. That being effective data management.
All of this new data is being created, or even stored, under one manageable umbrella. It’s disparate, it’s noisy, and in its raw form it’s often useless. So to uncover data’s aforementioned potential, businesses must take the necessary steps to “clean it up”.
That’s what the Any2 concept is all about. Allowing businesses to manage, govern and analyse any data, anywhere.
The first part of the Any2 equation, pertains to Any Data.
Managing data requires facing the challenges that come with the ‘three Vs of data’: volume, variety and velocity, with volume referring the amount of data, variety to its different sources, and velocity the speed in which it must be processed.
We can stretch these three Vs to five when we include veracity (confidence in the accuracy of the data), and value.
Generally, any data concerns the variety ‘V’, referring to the numbered and disparate potential sources data can be derived from. But as we need to be able to incorporate all of the varying forms of data to accurately analyze it, we can also say any data concerns the volume, and velocity too – especially where Big Data is considered.
Big Data initiatives increase the volume of data businesses have to manage exponentially, and to achieve desired time to market, it must be processed quickly (albeit thoroughly), too.
Additionally, data can be represented as either structured or unstructured.
Traditionally, most data fell under the structured label. Data including business data, relational data, and operational data, for example. And although the different types of data were still disparate, being inherently structured within their own vertical still made them far easier to manage, define, and analyze.
Unstructured data, however, is the polar opposite. It’s inherently messy and it’s hard to define, making both reporting and analysis potentially problematic. This is an issue many businesses face when transitioning to a more data-centric approach to operations.
Big data sources such as click stream data, IoT data, machine data and social media data all fall under this banner. All of these sources need to be rationalized and correlated so they can be analyzed more effectively, and in the same vain as the aforementioned structured data.
The anywhere half of the equation is arguably also predominantly focused on the variety ‘V’ – but from a different angle. Anywhere is more concerned with the differing and disparate ways and places in which data can be securely stored, rather than the variety in the data itself.
Although an understanding of where your data is has always been a necessity, it’s now become more relevant than ever. Prior to the adoption of cloud storage and services, data would have to have been managed locally, within the “firewall”.
Businesses would still have to know where the data was saved, and how it could be accessed.
However, the advantages of storing data outside of the business have become more apparent and more widely accepted. This has seen many businesses take the leap and invest in varying capacities, into-cloud based storage and software-as-a-service (SaaS).
Take SAP, for example. SAP provides one solution and one collated database, in favour of a business paying installation and upkeep fees for multiple softwares and databases.
And we still need to consider the uptick in the amount of businesses that buy customer data.
All of this data still has to be integrated, documented and understood in order for it to be useful, as poor management of data can lead to poor results – or, garbage in, garbage out for short.
Therefore, the key focus of the anywhere part of the equation is granting businesses the ability to manage external data at the same level as internal.
Effectively managing data anywhere, requires data modeling, business process and enterprise architecture.
Data modeling is needed to establish what you have whether internal or external, and to identify what that data is.
Business Processes is required to understand how the data should be used and how it best drives the business.
Enterprise Architecture is useful as it allows a business to determine how best to leverage the data to drive value. It’s also needed to ensure the business has a solid enough architecture to allow for this value to come to fruition, and in analyzing/predicting the impact of change, so that value isn’t adversely affected.
The best way to effectively manage Any Data, Anywhere, so that we can ensure investing in data management and analysis adds value, is to consider the ‘3Vs’ in relation to the data timeline. You should also consider the various initiatives (Data Modeling, Enterprise Architecture and Business Process) that can be actioned at each stage to ensure the data is properly processed and understood.
Any2 approach helps you:
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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.
In our last post, we looked at why any business with current, or future plans for a data-driven strategy need to ensure a strong data foundation is in place.
Without this, the insight provided by data can often be incomplete and misleading. This negates many of the benefits data strategies are typically implemented to find, and can cause problems down the line such as slowing down time to markets; increasing the potential for missteps and false starts; and above all else, adding to costs.
Leveraging a combination of data management tools, including data modeling, enterprise architecture and business processes can ensure the data foundations are strong, and analysis going forward is as accurate as possible.
For a breakdown of each discipline, how they fit together, and why they’re better together, read on below:
This post is part two of a two part series. For part 1, see here.
Data Modeling
Effective Data Modeling helps an organization catalogue, and standardize data, making the data more consistent and easier to digest and comprehend. It can provide direction for a systems strategy and aid in data analysis when developing new databases.
The value in the former is that it can indicate what kind of data should influence business processes, while the latter helps an organization find exactly what data they have available to them and categorize it.
In the modern world, data is a valuable resource, and so active data modeling in order to manage data, can reveal new threads of useful information. It gives businesses a way to query their databases for more refined and targeted analysis. Without an effective data model, insightful data can quite easily be overlooked.
Data modeling also helps organizations break down data silos. Typically, much of the data an organization possesses is kept on disparate systems and thus, making meaningful connections between them can be difficult. Data modeling serves to ease the integration of these systems, adding a new layer of depth to analysis.
Additionally, data modeling makes collaborating easier. As a rigorous and visual form of documentation, it can break down complexity and provide an organization with a defined framework, making communicating and sharing information about the business and its operations more straightforward.
Enterprise Architecture
Enterprise Architecture (EA) is a form of strategic planning used to map a businesses current capabilities, and determine the best course of action to achieve the ideal future state vision for the organization.
It typically straddles two key responsibilities. Those being ‘foundational’ enterprise architecture, and ‘vanguard’ enterprise architecture. Foundational EA tends to be more focused on the short term and is essentially implemented to govern ‘legacy IT’ tasks. The tasks we colloquially refer to as ‘keeping on the lights’.
It benefits a business by ensuring things like duplications in process, redundant processes, and unaccounted for systems and shelfware don’t cost the business time and money.
Vanguard enterprise architects tend to work with the long term vision in mind, and are expected to innovate to find the business new ways of reaching their future state objectives that could be more efficient than the current strategy.
It’s value to a business becomes more readily apparent when it enterprise architects operate in terms of business outcomes, and include better alignment of IT and the wider business; better strategic planning by adding transparency to the strategy, allowing the whole business to align behind, and work towards the future objective; and a healthier approach to risk, as the value (reward) in relation to the risk can be more accurately established.
Business Process
Business process solutions help leadership, operations and IT understand the complexities of their organizations in order to make better, more informed and intelligent opinions.
There are a number of factors that can influence an organization who had been making it by without a business process solution, to implement the initiative. Including strategic initiatives – like business transformation, mergers and acquisitions and business expansion; compliance & audits – such as new/changing industry regulations, government legislation and internal policies; and process improvement – enhancing financial performance, lowering operating costs and polishing the customer experience.
We can also look at the need for business process solutions from the perspective of challenges it can help overcome. Challenges including the complexities of a large organization and international workforces; confusion born of undefined and undocumented processes as well as outdated and redundant ones; competitor driven market disruption; and managing change.
Business process solutions aim to tackle these issues by allowing an organization to do the following:-
The Complete, Agile Foundation for the Data-Driven Enterprise.
As with data, these three examples of data management tools also benefit from a more fluent relationship, and for a long time, industry professionals have hoped for a more comprehensive approach. With DM, EA and BP tools that work in tandem with, and complement one another inherently.
It’s a request that makes sense too, as although all three data management tools are essential in their own right, they all influence one another.
We can look at acquiring, storing and analyzing data, then creating a strategy from that analysis’ as separate acts, or chapters. And when we bring the whole process together, under one suite, we effectively have the whole ‘Data Story’ available to us in a format we can analyze and inspect as a whole.
The countdown has begun to one of the biggest changes in data protection, but how much do you know about GDPR? In a series of articles throughout February we will explain the essential information you need to know and what you need to be doing now.
It stands for General Data Protection Regulation and it’s an EU legal framework which will apply to UK businesses from 25 May 2018. It’s a new set of legal requirements regarding data protection which adds new levels of accountability for companies, new requirements for documenting decisions and a new range of penalties if you don’t comply.
It’s designed to enable individuals to have better control of their own personal data.
While the law was ratified in 2016, countries have had a two-year implementation period which means businesses must be compliant by 2018.
The changes to data protection will be substantial as will be the penalties for failure to comply. It introduces concepts such as the right to be forgotten and formalises data breach notifications.
GDPR will ensure a regularity across all EU countries which means that individuals can expect to be treated the same in every country across Europe.
For processing personal data to be legal under GDPR businesses need to show that there is a legal basis as to why they require personal data and they need to document this reasoning.
GDPR states that personal data is any information that can be used to identify an individual. This means that, for the first time, it includes information such as genetic, mental, cultural, economic or social information.
To ensure valid consent is being given, businesses need to ensure simple language is used when asking for consent to collect personal data. Individuals must also have a clear understanding as to how the data will be used.
Furthermore, it is mandatory under the GDPR for businesses to employ a Data Protection Officer. This applies to public authorities and other companies where their core activities require “regular and systematic monitoring of data subjects on a large scale” or consist of “processing on a large scale of special categories of data”.
Data Protection Officers will also be required to complete Privacy Impact Assessment and give notification of a data breach within 72 hours.
At this stage it is unknown how the UK exiting the European Union will affect GDPR. However, with Article 50 yet to be triggered – the exit from the European Union is still over two years away and as such the UK will still be part of the EU in 2018. This means that businesses must comply with GDPR when it comes into force.
Penalties for failing to meet the requirements of GDPR could lead to fines of up to €20 million or 4% of the global annual turnover of the company for the previous year, whichever is higher. This high level of financial penalty could mean could have a serious impact on the future of a business.
Over the coming month, we will continue this series looking at how to get started preparing for GDPR now, why you need a Data Protection Officer and how GDPR will affect your international business.
With NoSQL data modeling gaining traction, data governance isn’t the only data shakeup organizations are currently facing.