Join Nick Mishko, Senior Data Analytics Team Lead at Calligo, as he delves into the world of property management analytics and Yardi data.

Discover how Calligo’s data analytics practice transforms Yardi data into powerful tools, enhancing operational efficiency for property management firms globally. From data extraction challenges to creating dynamic dashboards, explore the strategies and solutions that propel businesses forward.

If you’re navigating Yardi complexities or seeking to leverage analytics for your property management endeavours, this insightful discussion is a must-watch. Stay tuned for more insights from Calligo Shorts!

The post Unlocking Property Management Insights: Extracting and Analyzing Yardi Data appeared first on Calligo.

In this lively debate you will hear from Calligo’s Practice Leads as they discuss their key takeaways from 2023 and their data predictions for 2024 and beyond.

Topics discussed include:

The post Data Transformation Predictions for 2024 – Calligo Data Leaders Roundtable appeared first on Calligo.

In the age of information, businesses are confronted with an unprecedented influx of data, making effective data management critical for success. Two prominent solutions have emerged to address this challenge: data lakes and data warehouses. Each offers distinct advantages and use cases, catering to the diverse needs of modern enterprises. In this comprehensive exploration, we’ll dive into the fundamental differences between data lakes and data warehouses, and then we’ll shine a spotlight on Calligo’s Warehouse as a Service (WaaS) solution as a forward-thinking approach to data warehousing.

Data Lake vs. Data Warehouse: Navigating the Terrain
Data Lake: The Uncharted Waters

A data lake is a vast repository that can store structured, semi-structured, and unstructured data in its raw form. This makes it an ideal solution for organizations dealing with diverse data types and sources. Technologies like Apache Hadoop and Apache Spark are commonly associated with data lake implementations. Key strengths of data lakes include:

Flexibility: Data lakes accommodate raw and unstructured data, allowing organizations to ingest information without the need for predefined schemas.
Scalability: Built to handle massive data volumes, data lakes scale horizontally, making them well-suited for big data analytics.
Cost-Effective Storage: Storing raw data in a data lake is often more cost-effective compared to the structured storage in a data warehouse.
Data Warehouse: The Organized Harbor

In contrast, a data warehouse is a structured repository optimized for efficient querying and analysis. It stores data from various sources in a predefined, tabular format, enabling quick access for reporting and business intelligence activities. SQL databases are commonly used in data warehouse implementations. Key strengths of data warehouses include:

Structured Querying: Data warehouses excel in structured data querying, providing rapid access to organized information.
Performance: Aggregated and pre-processed data in a data warehouse enhances query performance, making it ideal for complex reporting and analytics.
Data Quality: Data warehouses enforce governance and quality standards, ensuring reliable and consistent data.

Calligo’s Warehouse as a Service (WaaS) Solution: Navigating Both Worlds
Amidst the dichotomy of data lakes and data warehouses, Calligo’s Warehouse as a Service (WaaS) solution emerges as a beacon of innovation, seamlessly integrating the strengths of both paradigms. This holistic approach empowers organizations to leverage the benefits of both data lakes and data warehouses within a unified platform. Let’s delve into the key features that make Calligo’s WaaS a game-changer:

1. Unified Platform:
   Calligo’s WaaS bridges the gap between data lakes and data warehouses, providing a unified platform for holistic data management. It allows organizations to store raw data in a flexible and cost-effective data lake while maintaining a structured and optimized subset in the data warehouse for analytical purposes. This integration enhances agility and ensures that the right data is available for the right purpose.
2. Optimized Storage:
   One of the distinctive features of Calligo’s WaaS is its intelligent storage management. Raw data can be stored in its native format within the data lake, minimizing costs associated with storage. Simultaneously, a curated and optimized subset of the data is stored in the data warehouse, ensuring high-performance analytics without compromising on the advantages of a data lake.
3. Advanced Analytics:
   Calligo’s WaaS is equipped with powerful analytics capabilities, enabling organizations to derive actionable insights from their data. The platform supports complex reporting, data visualization, and business intelligence, providing decision-makers with the tools they need to make informed choices.
4. Data Governance:
   Recognizing the paramount importance of data governance, Calligo’s WaaS prioritizes compliance with regulatory standards and maintains data quality across the entire data lifecycle. This ensures that organizations can trust the integrity and reliability of their data, fostering a culture of responsible data management.

Conclusion: Navigating the Data Landscape with Calligo’s WaaS
In the evolving realm of data management, the choice between a data lake and a data warehouse is often a complex decision based on specific organizational needs. Calligo’s Warehouse as a Service solution transcends this binary, offering a unified platform that integrates the best of both worlds. By seamlessly combining the flexibility of a data lake with the structured efficiency of a data warehouse, Calligo’s WaaS emerges as a pioneering solution for businesses seeking to navigate the complexities of modern data management. As organizations strive for data-driven excellence, the synergy of data lakes, data warehouses, and innovative solutions like Calligo’s WaaS can pave the way for a more efficient and insightful future.

For more comprehensive insights into data warehouse strategy, visit https://www.calligo.io

The post The Data Management Conundrum: Data Lake vs. Data Warehouse with Calligo’s Warehouse as a Service appeared first on Calligo.

ESG – good for the planet, good for business

In January 2023, the EU’s Corporate Sustainability Reporting Directive came into effect. Under its terms, all large companies and all listed companies (except micro-enterprises) must disclose information on the risks and opportunities arising from social and environmental issues, and their impact on people and the environment.

Set against this we have an AI revolution taking place – witness the activity on LinkedIn, with almost every other post lauding the benefits of some ChatGPT derivative or similar, leading to something of an AI feeding frenzy.

Looking through an ESG lens, the environmental impact of AI is huge. According to calculations by the specialist in sustainable data science, Kasper Groes Albin Ludvigsen, published in Medium at the end of 2022, ChatGPT could have consumed as much electricity as 175,000 people in the month of January 2023 alone. Equally, there are numerous articles that reference AI’s huge water impact.

One thing is clear. Whilst there can be many positive outcomes and by products from AI on ESG, the true end-to-end cost of this next wave of Digital Transformation is not yet well understood.

Given we are still trying to get to grips with the effects of the Industrial Revolution from an environmental perspective, how good is humankind’s track record of not repeating the mistakes of the past? How can we exploit opportunity without understanding the true cost and impact?

Wider business benefits of ESG

Developing an ESG strategy that is in harmony with your Digital Transformation yields multiple advantages. And whilst ESG reporting is now mandatory for corporations in the EU, doing so helps quantify the benefits that exist for every party:

• Investors. Many investors place great importance on ESG reporting and an overall strategy
• Customers. Consumers are increasingly concerned about the companies they place business with, and ESG is becoming far more important in their decision making
• Suppliers / Supply Chain. Companies are receiving more requests for information on their ESG credentials, capabilities and response. They must be able to demonstrate their end-to-end position when reporting, driving positive change throughout the supply chain
• Employees. Recruiting and retaining talent can be difficult, expensive and disruptive when there are issues with ESG policies. Research indicates that as many as 47% of employees would look for new roles if their organization is not proactive here
• Market reputation. Creating a strong reputation and a positive view of a company takes time and effort. Negative disclosures around ESG will quickly damage reputations, whereas positive ones will confer competitive advantage

Balancing potential conflicts between digital transformation and ESG

Detractors of ESG will point to the irony that a robust ESG process itself has an environmental impact: data centers in the EU consume more than 2.7% of the bloc’s electricity. And the Ukraine war has highlighted that the geopolitics of power supply will increasingly affect decisions on data processes and sovereignty – when Cloud storage and transference requires so many terawatts of electricity, securing a good price must be balanced against political and geographic risk.

Digital transformation is, by its very definition, a process of huge change. Done right it unlocks competitive advantage, delivers cost savings, drives productivity, opens up new opportunities and delivers compliance with ESG obligations. But done half-heartedly or implemented sporadically it will almost certainly be a huge waste of time, effort and resources.

Deloitte calculates that digital transformation could unlock as much as US$1.25 trillion in additional market capitalization across all Fortune 500 companies. However, done incorrectly, market value could actually be eroded, putting more than US$1.5 trillion at risk.

Prior preparation prevents poor performance

When it comes down to it, successful digital transformation requires only three things:

• An agreed plan
• The right tech platforms
• A joined-up approach
  

And whilst that sounds simple, it involves significant planning and project management resources. It’s not possible to retro-fix a digital solution onto your existing processes – a successful digital transformation requires a center-out approach, incorporating data privacy and protection and considering ESG objectives at the very heart of policy and technology.

When digital transformation is done correctly, “it’s like a caterpillar turning into a butterfly,” but when done wrong, “all you have is a really fast caterpillar.”

MIT Sloan Professor George Westerman

ESG at the heart of the digital transformation process

The comprehensive and insightful data analysis and management required to power your digital transformation needs a huge team of business experts, platform designers and technology specialists, all following a clear process:

• Develop an agreed, business-wide strategy
• Create and share a roadmap
• Define the metrics of success, and measure them
• Build user-friendly dashboards and data analytics
• Use optimal data platforms and cloud services
• Ensure data privacy and protection
• Set and track ESG targets. Not only does ESG need to be considered, it needs to sit right at the heart of digital transformation, informing and guiding the entire organization

Simply ‘ESG washing’ operations with fancy reports is both ineffective and expensive. That’s why Calligo ensures that every digital transformation we drive is engineered with careful attention to its environmental impact. Future-proofing your data use in a way that protects everyone’s future.

To help you navigate the expansive topic of digital transformation, we’ve put together a comprehensive eBook, outlining all the key considerations for your organization. And if all this sounds daunting, don’t worry –  we’ve seen plenty of similar challenges. Data privacy, for example. Once seen as a vague afterthought or something for someone else, today it takes center stage – the concept of Privacy by Design even has its own ISO standard (31700). Understanding the end-to-end ESG impact of Digital Transformation is heading the same way.

If you want to learn some more, or if you want specific advice, consultancy support or technical implementation, why not talk to our experts, who can get your digital transformation journey underway?

The post Powering up ESG through digital transformation appeared first on Calligo.

In Calligo’s latest Beyond Data podcast, Tessa Jones (Chief Data Scientist) is joined by Dr Ellie Graeden, Research Professor (Center for Global Health Science and Security) at Georgetown University. Here we explore some of the episode’s highlights:

• The inherent conflict of private data and the public good
• Protecting individual rights within federated learning
• The importance of effective communication and a common language
• Designing systems and policies that work together
• Focusing regulation on outcomes, not creating data siloes

At societal level, poor communication costs lives

Transitioning data across and between departments and data systems has historically been fraught with problems – who owns it? Who pays for it? Is it understandable and translatable into meaningful and actionable insights for the end user? 

Having worked extensively in disaster response, Dr Graeden has seen first-hand the potentially life-threatening issues that can arise when government departments’ data platforms produce incompatible outputs:

• If 20,000 people need water, how many pallets need to be shipped?
• If 10,000 electricity meters have been knocked out by a hurricane, how many people need feeding?

In such scenarios, identifying individuals amongst population-level data is crucial if the help provided is to be sufficient.

“We have to be able to really effectively move and communicate and share data that are relevant, in ways that they can get used by people all across the system”

Of course, any data system design should ensure privacy and protection for personal data. ‘Big data’ is still relatively new, and as such more powerful and widespread regulatory controls are now being introduced, although the US still does not have consistent requirements for how data should be handled. Fundamentally, meeting a population’s needs today, and planning for them tomorrow, requires the data of individual people to be analysed. Personal data must be shared quickly, effectively and all the while protecting individual rights. Data system design must therefore:

• Include all players
• Consider cultural constraints
• Keep out bias
• Ensure the right words and phrases are used
• Focus on the ‘so what’, why does it matter?

“Every single thing we experience can be captured as data”

Even the most mundane moments in our daily lives leave a digital footprint, we shed data everywhere. But when does ‘my’ data become public, or the property of the software developer or the service provider? VR headsets collect ephemeral data that is analysed and applied for that one end user, but if that data is assumed to fall under GDPR the potential to use it for positive outcomes is severely limited. For example, should authorities be notified if content viewed and generated is illegal or harmful? And what if that chip can detect if the user is having a stroke, is that data classified as ‘health’ data? Can it be used to alert the individual to their medical emergency without contravening legislation? What if your mouse clicks can detect the early stages of Parkinson’s? Should you, could you, be told?

“If you’re treating this data as health data, then they have a very different set of regulatory constraints. HIPAA isn’t going to regulate those because it’s not a health care provider or a health insurer”

Piercing the veil

The conflict between personal protection and public good is everywhere, and Dr Graeden believes that some new data laws will create problems for federated learning. Legislation has clear boundaries (speed limits, blood alcohol levels) whereas science deals in spectrums, probabilities and unknowns.

Deleting an individual’s personal data from the model breaks the system, contradicting what regulators are trying to achieve. The solution is to prioritize outcomes, not processes – it doesn’t matter whether you write the rules with a pen and paper, or with AI, as long as you write the rules. Expanding the framework by setting gradients of data availability affords protection for individuals, whilst making data available that informs better decision making for public bodies.

“Data is nothing more, nothing less, than an abstract description of our world. A useful and powerful language that can tell us things that other languages don’t”

Data can no longer exist in siloes if it’s to be useful to society

There is now a healthy global appetite for the discussion around data, thanks in the main to two recent developments:

• Covid gave us huge amounts of data about mortality levels, vaccination rates, hospitalisation trends – all of which were in the public consciousness every day
• AI and ChatGPT – articles and debates about the pros and cons are everywhere, discussion is not just in the scientific community

The key challenges now for data scientists are expectation management and communication – we need to be clear about aims and specific about context, as well as knowing what to leave out to avoid overwhelm and misunderstanding. Unfortunately, scientists are not always great communicators (using complex terminology and detail, rather than common parlance and generalization) as Covid demonstrated:

• Did having a vaccine mean you wouldn’t get sick? Or just less sick?
• ‘Everyone should wear a mask’ became ‘wear a mask if you can’. This was due to limited supply, but it appeared that the science was not clear

“The scientific approach means you never have an answer… we are trained as scientists to focus on the fact that we don’t know”

In fact, the only answer is that the right data, used consistently and communicated clearly, will always allow us to be prepared, not reactive. To make decisions for the public good that protect every individual.

You can find out more about the common language of privacy in our Rosetta Stone eBook.

You can also watch Tessa’s fascinating podcast with Dr Graeden below.

The post Making complex data available for the benefit of society appeared first on Calligo.

Artificial Intelligence’s ability to augment and support progress and development over the past few decades is inarguable. However, when does it become damaging, contradictory even? In our latest Beyond Data podcast AI’s Climate Jekyll & Hyde – friend and foe, Tessa Jones (our VP of Data Science, Research & Development) and Sophie Chase-Borthwick (our Data Ethics & Governance Lead) discuss exactly this with Joe Baguley, Vice President and Chief Technology Officer, EMEA, VMware.

Our speakers explore the multifaceted topic of energy consumption and AI – from whether all applications are equal for energy consumption (or reflecting if there are some ‘better’ than others), to creating visibility and responsibility of energy consumption for all stakeholders. Here we try to give clarity to some of the grey areas that were discussed.

Should we consider all applications equal?

“AI and machine learning are about huge things, huge data sets, huge computation actions … all of those have huge implications in terms of energy,” Joe observes, before dropping in hugely sobering stats such as the total annual energy consumption of bitcoin being the same as Norway. Even when considering the often-touted argument of 57% of the energy for bitcoin mining using renewables, Joe counters: “But those renewables could have been used for something else, right? Those solar panels… and those hydropower stations and those wind turbines, we could be using them for something else.”

This raises the ethical question of whether there should be stricter governance, standards, and precedent set on more ‘moral’ applications for their energy consumption. Should we be more closely considering the difference in energy consumption between server farms that support minimizing food waste versus those that are focused on mining digital currency, for example?

“Is there an opportunity for [greater] regulation?” Tessa ponders. Would this regulation help challenge the current status quo for all applications’ energy consumption being considered equal? While Sophie observes: “We’ve had certain European nations start to put rules around data center expansion, where you’re allowed and not allowed to build because of the capacity there, which isn’t regulating the use of it. But it does have that knock-on effect that if you literally can’t build the data center support, you have to start thinking about other ways to build [models].”

When considering Sophie’s point on alternative ways to build models, Joe notes: “We’re using AI to deal with the symptoms, but maybe there’s some better ways we could be using AI to deal with the cause as well”.

And this all raises the next question – who should ultimately be making these ongoing moral calls for the environment and energy usage?

Embedding Environmental, Social, and Governance (ESG) by design

Environmental, Social, and Governance (ESG) is shorthand for a framework that helps stakeholders understand how an organization is managing risks and opportunities related to environmental, social, and governance criteria. Our speakers untangle the idea of ESG and how companies could use it to help triage the different applications they use.

Joe asks: “Is there an ESG-led marketing opportunity here? Your AI might be the same as my AI, but my AI is better from an ESG perspective. They both get the same results at the same time for the same cost, but this one’s better from an ESG perspective, in terms of sustainability, in terms of social good, in terms of environmental.”

By placing more emphasis on ESG as the criterion for measuring impact and success, it could help with embedding sustainability in the heart of the application’s deployment, rather than a siloed approach. Sophie agrees: “We have privacy by design, we have security by design. Why not have ESG by design?”

Following on from this thought, our speakers consider the cost implications of AI and ESG with Joe observing, “There’s a lot of businesses right now that can’t afford AI because it’s expensive…but I believe they will come to a tipping point where they can’t afford not to”.

Are we over-prioritizing accuracy?

“There’s a hyper-focus on the accuracy,” according to Tessa. “It ends up not even being about the motivation for green, it’s a motivation for fast training, fast tuning. Unfortunately, it’s how most data scientists are motivated; be faster without having to compromise their accuracy.”

Often, the increase in accuracy can be mapped on a logarithmic graph. Good gains at first, but quickly tapering off to minimal increase. Is it useful to be that much more accurate, often by points of a decimal? “Some are good, more must be better … people just keep going, as opposed to saying actually good enough is good enough,” Joe summarizes.

Instead of chasing marginally better accuracy each time, we should be considering the application in a holistic view. The increase in accuracy might be 0.01%, but would cost heavily for energy consumption – is it worth it? Should we be better at exposing these costs more vigorously throughout a team so everyone can feel more empowered and have the visibility to interrogate more closely?

To hear about how our speakers untangle these controversial questions and more, tune in now to Beyond Data podcast episode 3: AI’s Climate Jekyll & Hyde – friend and foe.

The post The dark side of AI energy consumption – and what to do about it appeared first on Calligo.

When you see the phrase “Data Safety”, the chances are you think of Data Security. Most people do.

What is far less likely is that you think of the other two pillars of Data Safety: Data Privacy and Data Governance.

Clearly, all three pillars overlap. But Data Security seems to attract the most media attention, the most scrutiny and the most attention among business data leadership. In fact, when you compare the worldwide relative volumes of searches for the three terms, it shows an almost spookily even distribution:

And yet, when you consider the typical data lifecycle, all three pillars have an equally vital role in the protection of data at every single stage.

A simplistic – and by no means exhaustive – example…

As has been said about Data Security for decades, the only way to ensure robust and continuous Data Safety with every interaction is to design it into the fabric of your data workflows. It is after all well-known that neither security, privacy nor governance can be applied as afterthoughts – they have to be built into a business’s operations from the ground-up. Every process the data flows through, every person who interacts with it and yes, every technology on its journey.

And there is no technology more crucial to data’s journey through a business than your cloud environment. Your cloud sets the tone for how your data is treated.

How can Data Safety become part of my cloud DNA?

We asked our Chief Information Security Officer, Mark Herridge, for his guidance on how to make sure that your cloud environment sets the right tone for how your data is treated throughout the business.

Data Safety in your cloud environment

“Data safety really does entail security, privacy and governance. They go hand-in-hand, you can’t focus fully on one, and not the others – they are both supportive of and reliant on each other.”

Mark Herridge
Chief Information Security Officer, Calligo

The two key takeaways are simple: Data Safety must not be treated as synonymous with Data Security, and the entirety of Data Safety must be written into the fundamentals not only of your cloud environment’s design, but also how data is interacted with from it.

To find out more about data safety and the commercial benefits it can deliver to your organization.

The post How to design Data Safety into your cloud appeared first on Calligo.

2020 was a lot of things. Unexpected. Tough. Frightening. Frantic.

 

It was also revealing.

Most CIOs were asked to enable ways of working and doing business that they had not considered necessary before. Others had maybe always known such moves were wise, but had never been able to dedicate the time, resource or budget to such endeavours. Or, ironically, had never been able to prove the business case.

Either way, too many were caught under-prepared.

But why? When the moment of desperate need came, the solutions were available and the advantages were immediately clear – quickly solving the business case problem above. So why had they not been prioritised and invested in before?

Because most CIOs have technology strategies, not data strategies.

What’s the difference?

CIOs with technology strategies aim to address business objectives by selecting and deploying the most suitable platforms, tools and applications to deliver on them. Their focus will be on security gaps, cost reductions, efficiencies, capabilities & features, compliance and operational performance.

You may be reading that description and thinking it sounds ideal. Our research showed that 69% of CIOs would agree and find the description above quite familiar. And there is nothing technically wrong with such an approach.

But it is lacking.

A data strategy builds upon a technology strategy. It does not replace it. A data-focused CIO will still aim for everything a technology-focused one might, but then more besides. The difference is in the starting point.

As mentioned above, those who build a technology strategy will typically “take a step back” and consider the business objectives.

Meanwhile, data strategies – unsurprisingly – start with data. A data strategy requires a step even further back to consider how data enters, moves around and exits the business, what data is available to who, how safe it is, and what potential it has.

	What data do we have?
	Where is it, and where does it need to be?
	Who do we have data on?
	Who needs access to the data, and where are they – and where could they be?
	How is our data gathered and held, and is this acceptable?
	Is our data totally safe to use?
	How else could our data be used, and what might it show us or even do for us?
	What does our data tell us about how we serve our customers? What else could it show?

 

From these investigations, key shortcomings can be identified such as resilience gaps, cost efficiencies and security weaknesses, just as with a technology strategy.

The difference is that a data strategy will also reveal how data can and cannot be accessed and what those restrictions may mean; how safe it may be to use according to privacy regulations and other compliance requirements; where productivity is hampered; what datasets are actually available and what revelations may they hold; and even how it may be put to work through automation.

Solving all these shortcomings and exposing all these possibilities creates a flexible platform from which any business objectives can be supported, however they may change and however ambitious they may be. Meanwhile, a technology strategy built strictly around pre-defined goals is instantly exposed as soon as they change.

For more on the differences between the two, check out our infographic:

Why should 2021 be the year of the data strategy?

Because 2020 was the year of remote working and data access. Organizations scrambled to not only make data available beyond the corporate network, but also had to ensure that it remained secure and compliant, and that data privacy regulations were adhered to.

For those who had to date focused on technology, rather than data and its movement through the business, this was a massive ask, particularly when it came to security. In comparison, the core ethos of a data strategy is to create a platform that puts the right data securely and safely into the hands of the people who need it, however, and wherever they need access to it.

But if 2020 opened CIOs’ eyes as to how restricted their data had been, 2021 will be the year they capitalise on its new freedom. A data strategy is a far more forward-thinking, creative, innovative, open and customer-centric attitude than a technology strategy. And ultimately a more profitable one.

The freedom of data – provided within secure but non-restrictive controls – also allows for a closer understanding of customers’ journey through your business. This includes their entry point and motivations, if and why your team struggles to serve them, and when they might be considering churning or primed for additional services.

This in turn leads to improved NPS scores and customer retention, but also a far greater internal perception of the CIO and their team for their role in delivering such capability.

How do I shift to a data strategy in 2021?

The key to a data strategy is simple to state, but can be complex to accomplish: achieve a granular visibility of every data workflow and every data interaction.

The good news is that the effort is worth the rewards, as our research shows.

greater productivity

more capable of effective innovation

likely to grow profitability

 

The principle is that if you know every way in which your data is created, where it resides, how it moves, who uses it, where and why, then you can far easier find and solve any obstructions, inefficiencies or dangers.

And from there, every security weakness, compliance gap, productivity obstacle and cost sinkhole can be addressed, and new innovations and ambitions can be chased. All of which makes for a transformative new year.

Reinventing Digital Transformation Your guide for improving the safe and secure flow of data for greater visibility, productivity and cost efficiency   Learn More

The post What sort of CIO are you going to be? appeared first on Calligo.

Some businesses were more prepared than others, meaning many had to hastily adopt new technology and tools to enable the company to remain productive, have access to their data and be able to communicate and collaborate securely with staff, customers and suppliers.

Whilst these were short-term fixes, now is the time for CIOs and IT leaders to reassess the situation and look for a long-term solution. COVID-19 exposed how insufficient, out-of-date, restrictive, and expensive many businesses’ data infrastructure really was.

Now they must look at improving the safety and security of their data, improve accessibility, and ensure the right tools are in place for their workforce, as well as reducing not only IT spend but also supporting organization-wide cost reductions.

Now is the time for digital transformation. But with a twist.

Digital Transformation Post-COVID

There’s no more room for error. No more tolerance for inaccessible or poorly structured data. Especially as those organizations who were well-prepared as COVID-19 hit are accelerating further and further away.

Armed with new knowledge of what works and what does not, and know exactly where the shortcomings in their strategy lie, CIOs have the ammunition they need to build a digital infrastructure that will be impactful today and future-proofed.

But digital transformation has to be different this time.

This time, CIOs cannot resort to their historic “go to” approach of simply deploying new technologies that meet the business needs no matter how innovative or well-integrated they may be. Under the heightened scrutiny of recent months, this “technology down” approach has been shown to be too short term, narrow and often expensive.

IT leaders need to instead make data the starting point and create a strategy that starts with how data needs to move around the business, and in ways that support workforces’ needs today and also their new ideas – and most of all, securely and compliantly so that every data interaction is safe.

This approach – and this approach alone – will allow businesses to rebuild quickly, reliably and competitively.

What sort of CIO are you going to be in 2021?   Read why CIOs must become data-focused and build data strategies over outdated technology strategies Learn More

What’s the difference between a technology strategy and a data strategy?

A technology strategy looks at business’ requirements and identifies which tools, platforms and policies can best meet those needs.

The infrastructure and tools will be tightly cost-controlled and reviewed to prevent overspending, especially on superfluous licenses or features. And the entire IT environment will be protected against internal and external IT security threats. In some cases, there will also be organization-wide processes and policies in place that follow recognized standards such as ISO 27001, as well as frequent awareness training to ensure processes are understood and followed.

This looks like the right approach. It is certainly not incorrect. But for cost-efficiency and competitiveness today, it is not enough. It fails to address the wider and more modern requirements of data – including its secure global accessibility, its privacy, its ethical use, its continuous governance (not just “point in time” audit compliance) and its suitability and readiness for automation and insights.

These are the extra factors that enable an organization to put data safely in the hands of the teams that needs it to be more productive, reduce costs and earn greater customer loyalty and value.

What business benefits does a data strategy provide?

Productivity:

Organizations that improve their access to data and make their data more visible have a better understanding of what is relevant and what is not. This provides clarity on data workflows, enabling businesses to focus on the data that will deliver the most returns. It also enables more ambitious organizations to introduce automation to remove risk and error as well as providing more time for employees to focus on new tasks.

*Statistics taken from Calligo’s survey of 500+ businesses across Europe and North America,
comparing those with data strategies to those with technology strategies

Customer satisfaction and retention:

Data strategies have an enormous impact on client experiences, from initial engagements to the delivery of services to clients.

Having a greater visibility of data workflows enables businesses to have a clearer and more accurate picture of every engagement and touchpoint, meaning organizations can offer a more personalized experience, tailoring services and communications, identifying any failings before they manifest, or even just delivering a faster and better service – all of which lead to better client satisfaction.

Profitability:

Needless to say, if you have a more productive team and happier clients, this will lead to an increase in profitability. Businesses that adopt a data strategy are twice as likely to see a 50% increase in profitability than those who have taken on a technology strategy.

Reinventing Digital Transformation

Whether your digital transformation project seeks to improve productivity and data accessibility, reduce costs, or ensure continuous IT security and data privacy adherence, Calligo can help.

Calligo is the world’s first end-to-end managed data services provider. We have a global track record in improving organizations’ productivity & profitability by making their data more available, secure, and safe.

We will guide and deploy your entire digital transformation project, ensuring your immediate and long-term objectives are met. Our data strategy consultancy teams include experts in:

	Strategic consulting			Infosecurity
	Cloud technology			Data privacy & data residency
	Machine learning & automation			Data architecture
	Business productivity & data workflows

The post Reinventing Digital Transformation appeared first on Calligo.

What if every platform you use to communicate with your staff and your customers went down right now?

Companies rarely get an advanced warning that a disaster is about to strike. There is no time to prepare, and no time to protect your company from the fallout. That time has passed, and for companies without a business continuity plan, all that is left to do is lament how poorly prepared they were.

Having a business continuity plan gives your company the best chance at success and survival during a disaster. The lack of a business continuity plan does not just mean that your company will take longer to get back up and running; it could just as easily mean that you go out of business for good.

What is a Business Continuity Plan (BCP)?

A business continuity plan (BCP) aims to maintain all business systems and operations at 100% in the event of a major disruption, ranging from a cyberattack to extreme weather events. It outlines the procedures and processes your company will follow in the event of a disaster and covers your business processes, IT infrastructure, human resources, physical facilities and more.

Many companies confuse business continuity with disaster recovery (DR) and backup, but they are not the same. A DR plan focuses mainly on restoring your IT infrastructure and operations after a crisis, but a business continuity plan encompasses the stability of the entire company.

• How will you communicate with your staff about what has happened, what needs to be done and then keep them updated?
• How will you communicate with your customers and stakeholders?
• Will your team be able to continue serving customers and making sales?
• How quickly will your company be back online? And is this fast enough?

A business continuity plan looks at all of this, and plenty more, so your company can effectively deal with the crisis, and minimize the losses and the risk of terminal damage.

How to create an effective business continuity plan

Creating a business continuity plan starts with assessing your business processes. Map your business processes top to bottom across sales, HR, suppliers, internal and external communications, product/service delivery, accounts etc.

No process is invulnerable, so make sure you have an overview of every process, every individual involved and every reliance, whether that’s data, equipment, applications or personnel.

Business Impact Analysis (BIA)

A business impact analysis (BIA) identifies the impact of a sudden loss of any business functions, usually quantified in an operational cost and then a financial cost.

For each of your business processes, identify the points in time or scenarios when interruption to any of them would have the most impact, such as the end of the month, start of the season, particularly busy periods etc.

Now assess what the operational costs of this disruption would be, e.g. lost or delayed income, increased expenses, fines, contractual penalties, customer dissatisfaction, loss of communication. How does this assessment vary depending on the duration of the disruption? What if it lasts for an hour? A day? A week? How long can you bear disruption?

Now assess the financial impact of these operational costs, and for each of these durations.

This business impact analysis will help you to evaluate which of your processes are essential and need to be maintained regardless of the situation and over what timeline. It will also help you to identify the non-core processes that your company could outsource to improve resilience.

Business resources

Recovering critical or time-sensitive business processes requires resources. Before disaster strikes, your company needs to know what resources it has and what resources it will need to carry out recovery strategies and to restore normal business operations.

Resources can be within the business, or can be third-party:

• Employees
• Office space and equipment
• Data (electronic and hard copy)
• Technology (servers, computers, communication equipment, software)
• Production facilities
• Inventory
• Utilities

Estimate the resources that your company will need in the hours, days and weeks following a disaster.

Another sensible step is to outline the key personnel required to implement a business continuity plan, who should have access to it, and where it is available as well as the contact information for any emergency responders, data backup providers, technical experts, recovery locations etc.

Recovery plan

It is now time to develop a plan to maintain or recover your business operations in the event of an incident.

For each of your business processes, review what is necessary to recover to at least minimum acceptable levels of operations following a disruption. Staff with an in-depth understanding of the business functions and processes are best positioned to determine what strategies will work.

Ensure that your recovery strategies identify the resources required, including people, facilities, equipment, materials, data and information technology, and in many cases, the contracted third parties that can help or will be required.

Test and review your business continuity plan

Testing your plan is the only way to know if it works. But tests are about more than just confidence in its reliability. A real disaster will likely throw up additional, unforeseen scenarios, and having a thorough plan in place that you have rehearsed and fine-tuned, means you can adapt more easily to any unanticipated problems.

Many companies test their business continuity plan two or three times a year. How often you schedule your tests depends on your company and its degree of change. Be prepared for tests to reveal flaws in the plan, inadequacies against how your business has changed, or individual unpreparedness. Crucially, therefore, every test should be followed by a thorough review of performance and the plan’s suitability.

Training

Finally, even the most effective plans will fall short if they cannot be implemented properly and promptly. Even the most well-built plan relies on teams being familiar with their roles and in place to execute it.

Make sure that any personnel included in your BCP are trained on the plan and understand their role and responsibilities should the time come. Regularly provide refresher briefings and training and make sure they understand not only their individual responsibilities, but also how their role contributes to the bigger picture.

Get started

If your company needs to mitigate the risk of an IT disaster, cyberattack or other business disruption, get in touch with the Calligo team today for a free Business Continuity Plan and Disaster Recovery consultation.

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