Digital transformation has aided businesses in embracing change and remaining competitive in an increasingly digital world.

Data is the sole driver of today’s world. It has become the most valuable asset, and without it, one cannot hope to prosper in the present congested market. Companies must efficiently use their data because it can be a differentiating factor for corporate growth. This necessitates the effective integration of data analytics and Artificial Intelligence (AI) into business processes.

Integrating big data analytics allows for faster, more informed decision-making and access to real-time data. It has the ability to be a game-changer in businesses’ efforts to transform digitally. Numerous data analytics services companies are extended their services and helping enterprises in creating new business models and achieving great heights. 

Implementing big data analytics and AI into existing business models can yield real results, allowing firms to undertake a successful digital transformation. The transformation process aims to use digital technology to build or modify consumer experiences, culture, and business processes, to suit changing customer wants and market conditions.

This is where AI comes into play. It has the potential to help businesses create a digital product development life cycle, become more imaginative, agile, and adaptable than ever before.

How Big Data Aids Digital Transformation?

Digital transformation has aided businesses in embracing change and remaining competitive in an increasingly digital world. The usefulness of big data in digital transformation is determined by an organization’s ability to combine the two to enable business process digitization and automation. This digitalization and automation leads to digital transformation in product development, more efficiency, new business models, and innovation.

Businesses can also use big data analytics to get precise information about an individual or various groups of clients. This can include information such as which websites do they spend time on, what they purchase, purchasing frequency, etc. Businesses can use this data to make improvements to meet their customers’ future wants while also creating goals on how to achieve those demands. As a result, to complete their digital transformation, organisations must use big data and data analytics.

Artificial Intelligence and Digital Transformation: What’s the Connection?

When Artificial Intelligence became a part of many organisations’ business goals, the digital transformation went another step forward with the ability to integrate different systems and automate various daily tasks. These technologies are crucial in the digital transformation because they allow your company to make better use of the data it collects in a variety of ways.

As well as resulting in faster and more efficient operations and, as a result, increased output. AI enables us to make use of all of this data to propel the organization forward, whether by improving current products and services or exploring new inventive initiatives.

How Big Data Analytics and AI can help you create value through digitization? 

Emerging technologies, notably AI, are slowly infiltrating the everyday lives of businesses all around the world, much like the internet revolutionised the business world in the past.

Data analytics services for small business and medium businesses are also offered by numerous companies. Having said this, the question is no more when will all small and medium-sized businesses be digitally transformed, but which technologies will be important and prioritised. AI and big data may be employed in a variety of industries by businesses.

Digital transformation must be used to add value to a product, with artificial intelligence detecting information to assist the experts involved in various stages of the digital product development process, such as design, execution, and delivery.

With everything digitalized, you may start looking at the phases of the project that need to be tweaked; for example, AI in the design phase increases research, development, and forecasts the future steps.

In the execution phase, continuous maintenance is an important stage, and this is where AI and Machine Learning can assist. These are real-time replies to the research. When it comes to delivering a product, it’s all about the customer experience. Businesses can leverage data analytics services and solutions to ensure they are on the right track.

Much has been stated about how AI and Big Data Analytics can enable businesses to stay ahead of the curve in their digital transformation, giving them a competitive advantage over their competitors. Let’s take a look at why organisations need to be digital in product developmentand why should they use big data analytics and artificial intelligence in their digital transformation efforts:

• Using Data Analytics Services, businesses can readily measure and model risks using big data tools.
• By automating and optimising common procedures and procedures, businesses can save time and money.
• Many big data analysis technologies enable businesses to have a deeper understanding of their customers and actively develop loyal customers rather than simply responding to their needs and inquiries.
• Create quicker business decisions based on cognitive technology outputs.
• Businesses can learn about customer-related trends and patterns and focus on client retention by utilising big data analytics.

In a nutshell, big data analytics and AI may be used in a variety of ways to assist businesses in achieving successful digital transformation. Companies must keep in mind, however, that digital business transformation is a continuous process, and they must continue to come up with new strategies and leverage big data analytics services for enhancing business intelligence and keep up with the changing environment even after they have achieved success.

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Big Data Exchange (BDx), a pan-Asian carrier-neutral data center cluster, today announced a new partnership with Cogent Communications in the company’s Singapore (SIN1) data center facility. Cogent Communications is among the top three globally ranked carriers specializing in providing IP transit, dedicated internet access, ethernet transport, SD-WAN and colocation services.

Singapore boasts the highest megawatt per capita globally, and its desirable and strategic location in the center of Asia empowers enterprises to access Asia-Pacific’s most rapidly emerging markets easily. Conversely, the high demand for colocation space makes data center availability hard to come by for new customers. Acquired by BDx earlier this year, SIN1 is one of the facilities still available to service new customers in capacity-constrained Singapore.

SIN1 houses 1,500 racks with a 6 MW power capacity within 14,400 square meters. It has been awarded an UpTime Tier 3 Design Certificate, as well as holding SS564 GreenMark Gold Plus, TVRA, ISO27001 and PCI-DSS certifications. Construction to add four floors and an additional 8 MW of capacity has also started. Upon completion, the SIN1 data center will house a total of 14 MW of capacity, making BDx one of the few data centers still able to service new customers in Singapore. The purpose-built servers within the SIN1 data center enable businesses to gain the robust insights needed to power their digital transformation. The BDx 360 portal provides customers with a holistic view into their infrastructure from any device or location.

The addition of Cogent as a new carrier in the BDx SIN1 facility will allow BDx customers to benefit from Cogent’s extensive footprint, network capabilities and range of services. As a leading telecoms carrier, Cogent is available as a connectivity option to all BDx customers regardless of the type of colocation services they require, along with offering high standards of operations and support.

“This new agreement with Cogent marks the beginning of a strategic partnership which we look forward to developing,” says Sona Singh, Business Development Director at BDx. “We are proud that Cogent has chosen BDx to partner within the important Singapore market. Together, we offer an even wider range of services for our customers. We are pleased to welcome Cogent into our SIN1 facility and look forward to expanding our partnership in the near future.”

Cogent chose to partner with BDx because the company provides the widest range of flexible, secure, on-demand connectivity options at each of its facilities. The SIN1 data center provides critical connectivity for customers looking to expand into the Southeast Asian market. Cogent, one of the largest Tier 1 IP providers globally, selected BDx due to the company’s state-of-the-art operations and customer support.

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Sunline and Huawei have jointly released the Digital Loan One Box solution, a global contactless solution for financial services.

According to the two companies, the solution will be rolled out across countries and regions, such as Southeast Asia, the Middle East, Latin America, and Africa, where financial inclusiveness is urgently necessary.

Digital Loan One Box adopts an open strategy, allowing financial institutions to launch a wide range of contactless services, such as online loan product campaign, customer E-KYC, risk assessment, fund disbursement and post-loan processing.

The solution responds to the urgent need from both FIs and their customers for digital financial services. This requirement is ever more pressing amid the pandemic that many countries continue to battle.

As the cornerstones of national economies, financial institutions need contactless, digital, and online financial services, especially in the current situation. However, global banks face similar challenges in the course of digital transformation: huge costs and risks to replace legacy systems, limited budget and uncertain return on investment for transformation, lack of open business and technology experience, increasingly fierce competitions, and more demand of contactless service driven by the pandemic, etc.

Contactless finance refers to services where customers and banks do not need to interact face-to-face. Via omni-channels, it supports plug-in-based services, built-in businesses and out-of-the-box features.

“Amid the ongoing pandemic, the solution comes at the right time for financial institutions. It can satisfy the demands of efficient lending and loaning for both financial institutions and customers during such special times. Within this industry, we will continue to develop a global ecosystem and scenario-based solutions from 2C to 2B with global partners,” said Jason Cao, President, Global Financial Services Business Unit, Enterprise Business Group, Huawei. “Together with customers and partners in the industry, we will start with 5G and seize the opportunities across five major tech domains – connectivity, cloud, computing, AI and industry applications. Ultimately, we will provide scenario-based smart solutions and technologies to facilitate the digital transformation for financial customers.”

Hongan Li, Senior Vice President, Sunline, added that: “Sunline is a global banking IT service company. We keep innovating to lead in FinTech and enrich the interconnection of life. During COVID-19, we realised the urgent need for technological innovation, digital services, and open financial services global wide. In light of this, Sunline and Huawei are now launching the Digital Loan One Box Solution. The solution is designed for financial institutions and customers, especially for those lacking technology capabilities. It is based on Sunline’s enterprise-level microservices framework for banks. The agile platform features high flexibility, loose coupling, high performance, and distributed deployment. It meets the dynamic horizontal scaling requirements of financial institutions of any size at both the business and system demands. Our aim is to provide a solution that is practical and helpful in this extraordinary time.”

The Digital Loan One Box Solution is built on Huawei FusionCube Hyper-Converged Infrastructure, adopting the enterprise-level microservices banking framework EDSP released by Sunline in 2020.

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Blockchain today is still in its infancy, and its mainstream value is yet to be realised. While, it’s for sure that blockchain will disrupt the existing solutions, not only in industry and commerce but in almost all aspects of our day-to-day lives, it cannot do so just by itself. Same holds true for Internet of Things (IoT) and Artificial Intelligence (AI). The underlying fact is that to get the real value new-age emerging technologies such as blockchain, AI and IoT have to work in tandem. As we begin to understand the new normal in the midst of the corona pandemic, it will be important to draw value from any digital transformation that firms undertake. Businesses will have to think beyond their domain and scope to provide services which are of actual value to consumers.

How can this happen? IoT has brought new and cheaper ways to communicate with ‘things’ which was not fathomable in the past. Blockchain, with promise of immutability, transparency, security, interoperability, etc., allows us to exploit otherwise unused resources, trade the un-tradable, and allow new ecosystems that were not possible before. The new entrant AI (inclusive of machine/deep learning, vision, NLP, robots or autonomous machines etc.) has already started to deliver great value to many industries, so much so as to reduce or even replace the human element. Further advancement in 5G communication is a positive catalyst to this ecosystem.

However, these technologies, with a disjointed ecosystem or industries’ siloed approach towards them, may not reach their full potential. In the above combination, ‘data’ becomes the common driving factor. While IoT is producing data from new sources and sensors, blockchain is safeguarding and ensuring immutability, and the AI layer on top is helping deliver new business meanings and outcomes in almost real-time. In summary, data value chain comes from new technologies enabling collection, sharing, security, immutability, analysis, and automation of decisions with minimal human involvement.

Let’s run this model on a practical consumer problem of provenance – the classic ‘Farm to Table’ use case. The big questions that need solutions are with respect to quality, credibility, genuineness, safety, increase in efficiency and warranting correct distribution of revenue. IoT takes care of conditions maintained in farms with respect to temperature, humidity, soil nutrients and growth progress, and also conditions at processing centres and logistics. All this information can be stored on blockchain-based smart contracts. AI-based engine on top of this, with feeds from weather systems, etc., can trigger and automatically execute smart contracts and take required action based on pre-agreed rules, including payments, etc. In an adverse event like an outbreak at any stage, the source could be easily traced and isolated. Next, this can be extended to insurance and forward commodity trading using a trade setup, thus bringing real value from agriculture, supply chain, financial services, insurance and other industries combined.

IoT has come a long way in improving the type of sensors, size and cost and even their usage in some industries; the real consumer centric benefits can be manifold. AI faces the challenge of accuracy, trust and confidence over replacement by the human cognitive mind. Building such ecosystems without regulatory pressure, is not easy if not impossible. This is one of the primary factors for blockchain and other similar transformative technologies not gaining mainstream acceptance or adoption.

Let’s also keep an eye on ‘Quantum Computing’ breakthroughs, as this not only threatens the key features of these emerging technologies, but will severely impact best of encryption, security and cryptography that exists today. Which means any industry, digital ecosystems, IT infrastructure will have to evolve at a rapid pace before they get negatively impacted.

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The pandemic has changed the world, accelerating the pace of digital transformation and forcing businesses to find new efficiencies — not only in cost cutting but in decision making.

That’s why business leaders love AI, because it can help them make better and faster decisions by providing quicker and more accurate insights. It can tell them what customers really want — and what they want right now.

There is a problem though: Consumers don’t love AI. In one recent study, only 25% said they would trust an AI decision more than one made by a human, and less than 30% were comfortable with businesses using AI to interact with them.

Consumers are skeptical about AI because they don’t understand it. Many are scared by what they read and hear and not all their fears are misplaced. 

Unintentional built-in bias leads algorithms to make decisions, like credit denials, that unfairly discriminate against some minority consumers. It’s also probable that jobs will be lost to AI. What industry does to mitigate both of these problems will be key to maintaining consumer trust. 

Many business leaders attempt to impress customers merely by devising an “AI strategy” or building an “AI system” — and trumpeting it loudly. But customers are put off by this approach. 

What consumers value is the convenience, speed and choice delivered by AI working behind the scenes when they’re doing things like shopping or seeking health advice. 

They’re impressed by better products and services, greater convenience and better value for money, all of which AI delivers. The disconnect means business leaders need to think about their AI efforts differently, and spend more time educating consumers on how AI benefits them.

Not to be denied: AI is doing great things

Few shoppers realize the overnight arrival of their online purchases depends on AI algorithms getting items packed and shipped expeditiously. How many grasp that AI enables a customer service agent or chatbot to make quick-fire responses to their queries and requests? Few probably understand AI is locating the nearby Lyft driver within seconds of a tap on a smartphone.

The workings of AI are mostly unseen in the financial industry, where they very much benefit both institutions and customers. Take fraud detection. Some earlier protections were simple, blunt instruments. 

Sure, they stopped fraudulent transactions, but they also blocked many legitimate ones (“false positives”), often resulting in terrible customer experiences. Today, AI-based platforms pinpoint fraud faster and better, reducing false positives by more than half. That equals more legitimate transactions and fewer unhappy customers.

Within organizations, AI is eliminating repetitive tasks and enabling creativity. Business leaders should be asking, how much drudgery can AI push out of the system so my employees can do more creative work?

Diversity and discretion

What lessons can business leaders draw from all this? One is to tackle the bias issue — the danger that flawed algorithms make decisions unfairly, disadvantaging consumers. 

The first step is to understand that the bias isn’t in the AI but in the data, whether it’s data used initially to train the algorithm or data entered to generate insights. The best way to minimize bias is through diversity — finding and using the widest possible range of datasets. Not just quantity but variety. 

In human interaction, we fight bias by talking to people with different perspectives, by working with people that challenge and question the status quo. The same approach needs to be taken to the data we use to inform our algorithms.

Different groups of business stakeholders need different talking points when it comes to AI. Analysts and investors may be impressed with tales of tech capabilities, but customers just want to hear about better services or products, and that their data is being strongly protected and ethically used. 

We’re more likely to build trust by flat out saying, “We’re not going to compromise your data.” 

Business leaders need to understand AI’s limitations. AI is a tool, not a product, a strategy or a business model. Powerful it may be, but AI is only assisting us in doing the things we’ve always strived to be: smarter, faster, more efficient and, hopefully, more trustworthy

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Robotic process automation (RPA) has grown from a “good to have” to a “must-have” arsenal in the digital transformation of business, proving itself to be a crucial “not-to-be-missed” ingredient for business continuity through its enhanced functionalities.

For companies to begin their automation journey, there is no time like the present. Robotic process automation can alleviate the strain of repetitive tasks within their workforce, free departments for more innovative thinking, and align their organization with critical digital transformation practices.

Through RPA, companies have a unique opportunity to create agility and reform their existing business processes. Successful use of automation would bring about a definite competitive advantage- The organization will be more agile and nimble than its competitors and reach directly to its customers faster by focusing its resources on the more important critical thinking workflows.

Robotic Process Automation credits itself to automate repetitive rule-based tasks that involve structured data extracted from spreadsheets and databases. The problem being about 80% of the current data available in the unstructured format including PDFs, word documents, emails, images, videos paves way for the perfect collaboration of RPA with AI. With the advent of technological advancements, the capabilities and bandwidth of RPA have evolved into intelligent automation capable of streamlining end-to-end business processes, as well as repetitive/rule-based tasks.

This exciting automation future will envision a rewarding partnership between digital bots and the human workforce to achieve a level of productivity that makes employees invaluable. With assisted bots overseeing repetitive, rule-based workflows, human resources can realign their time in critical, thought-provoking, and transformative task schedules.

Also known as Intelligent Process Automation (IPA), this enhanced and innovative version of RPA comes with cognitive capabilities that enable it to effectively deal with unstructured data sources. Combining IPA with RPA allow companies unleash the power of intelligent innovative automation that involve both structured and unstructured data.

A key driver to this tectonic shift would be the widespread availability of AI and automation products to businesses of all stages and sizes. Currently, organizations like Microsoft and Amazon are levelling the playing field by offering their tools on a subscription, or “as-a-service” basis. By doing so, companies no longer need to spend large amounts of money on creating their tools. Platforms once only accessible by enterprise-level organizations have become affordable for small- to medium-sized businesses as well.

The future of RPA will involve the application of Artificial Intelligence and related new technologies, including advanced Computer Vision, unattended robotics, and integration with third-party cognitive services from Google, IBM, Microsoft and ABBYY to Robotic Process Automation. This convergence would actualize automation capabilities that would elevate business value and competitive advantage for stakeholders amplifying the power of RPA technology.

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Big data is nothing but the confidential information storage center, which needs the support of AI to manage the huge volumes and function seamlessly.

In most organizations, big data storage is spread across different computers, on-prem, or on the cloud. The analysis of such an incredible and useful source of data, is best done with AI based tools and applications.

The insights derived by AI tools from both structured and unstructured data, effectively have the power to change the course of a company’s business growth. The World Economic Forum in 2016 has estimated an increase of stupendous US$100 trillion in social value and global business by 2030. Most of this growth will be driven by the power of data collated today!

A report suggests that, on average, every day, humans create about 2.5 quintillion bytes of data. If all these data are well-utilized, they will surely allow humans to get an improved view of the future. In business, this could be the difference between survival, or liquidation- or explosive growth.  To be precise, data is valued to assets, which allows companies to get a hint of the future through advanced analysis.

AI technologies will probably be one of the biggets drivers for this growth, if studies from  PwC and McKinsey are anything to go by. They estimate the increase in AI tools business to touch US$15.7 trillion, and be around US$13 trillion of the annual GDP by 2030. However, there are a few significant challenges that companies face concerning big data, which could be easily resolved by adopting technological changes:

Diversity in IT source system

Storing data is always a complicated process, and securing, maintaining/managing it is even more difficult. The average Fortune 500 Company has thousands of enterprise IT systems across diverse formats, with mismatched references across data sources and duplication errors. Such diversity only complicates the situation and creates chaos.

High-frequency data management

Data flow is real-time, so critical issues like censoring of data still stay unspoken. So, high-frequency data management not only complicates the process but multiplies the vulnerabilities and risks. AI can help sort and censor the data as and when they flow in.

Organizing data content from diverse sources

Since big data is gathered from varying and wide varieties of sources, their formats are different, and most of it is unstructured.  So, even to structure the data into analyze-able bites is challenging and involves a lot of tools. This is merely to differentiate them and put them across diverse channels before conducting the in-depth analysis. One more added issue is data clarity, as some files don’t even comply with the set minimum clarity bar.

Looking at all these issues, AI can be the superhero sorting them all. Enterprise analytics and IT team need to provide advanced tools to empower employees with diverse levels of data science proficiency to function effectively with large data sets, and perform smart predictive analytics using a unified vision.

 Resolving the big data issues

Data Scientists could potentially be the magicians that will derive insights form the humongous amounts of Big Data in the market today. The predictions and insights could be the deciding factor for nay industry. Many CIOs feel ML algorithms are of great use as it facilitates the necessity to receive new data, generate outcomes, and have some decisions or actions to be made based on the outputs. Multiple algorithm libraries are widely available to data scientists today, and they can use these to create the highest quality actionable insights.

Big data issues like unstructured data, bad clarity content, data lakes, etc. are problems that could be demystified with the help of ML, data science applications, and with efficient application of AI tools.

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As the world struggles to achieve the UN’s Sustainable Development Goals (SDGs), the need for reliable data to track our progress is more important than ever. Government, civil society, and private sector organizations all play a role in producing, sharing, and using this data, but their information-gathering and -analysis efforts have been able to shed light on only 68 percent of the SDG indicators so far, according to a 2019 UN study.

To help fill the gap, the data science for social good (DSSG) movement has for years been making datasets about important social issues—such as health care infrastructure, school enrollment, air quality, and business registrations—available to trusted organizations or the public. Large tech companies such as Facebook, Google, Amazon, and others have recently begun to embrace the DSSG movement. Spurred on by advances in the field, the Development Data Partnership, the World Economic Forum’s 2030Vision consortium, and Data Collaboratives, they’re offering information about social media users’ mobility during COVID-19, cloud computing infrastructure to help nonprofits analyze large datasets, and other important tools and services.

But sharing data resources doesn’t mean they’ll be used effectively, if at all, to advance social impact. High-impact results require recipients of data assistance to inhabit a robust, holistic data ecosystem that includes assets like policies for safely handling data and the skills to analyze it. As tech firms become increasingly involved with using data and data science to help achieve the SDGs, it’s important that they understand the possibilities and limitations of the nonprofits and other civil society organizations they’re working with. Without a firm grasp on the data ecosystems of their partners, all the technical wizardry in the world may be for naught.

Companies must ask questions such as: What incentives or disincentives are in place for nonprofits to experiment with data science in their work? What gaps remain between what nonprofits or data scientists need and the resources funders provide? What skills must be developed? To help find answers, TechChange, an organization dedicated to using technology for social good, partnered with Project17, Facebook’s partnerships-led initiative to accelerate progress on the SDGs. Over the past six months, the team led interviews with top figures in the DSSG community from industry, academia, and the public sector. The 14 experts shared numerous insights into using data and data science to advance social good and the SDGs. Four takeaways emerged from our conversations and research:

1. Support the hidden work that makes effective data science possible.

Data scientists spend nearly half their time making sure their data is reliable, clean, and well organized. But too often, social sector organizations don’t have the resources to invest in essential steps like standardizing or digitizing data, which are critical to ensuring the quality of their data science projects.

Their struggles are understandable. Depending on the institution, preparing the “right data” for the task at hand could range from digitizing paper records to synchronizing knowledge management systems across different teams. This behind-the-scenes work can also involve the difficult task of developing and using standards for interoperability and data sharing. Some organizations are trying to make this easier. Carl Elmstam and Roderick Besseling, digital development experts from the Swedish development agency Sida and the Dutch Foreign Ministry, referenced the International Aid Transparency Initiative (IATI) to create a global standard for publishing data about humanitarian funding and projects. Such standards are important for coordinating complex, highly collaborative projects. For example, effective global research on COVID-19 requires harmonizing case data related to the disease.

The experts we spoke with said that tech companies can help civil society organizations struggling with this initial stage of a big data project by:

• Elevating awareness about the importance of organizing and standardizing data.
• Requiring funded groups to adhere to standards like those put forth by the IATI.
• Offering financial resources for social sector organizations to do this behind-the-scenes work.

2. Help partners build their data culture and digital transformation strategies.

A digital transformation strategy is an action plan to help an organization improve how it leverages digital tools and techniques, including data, to advance its goals more efficiently and effectively. By taking a big-picture view across the often deeply entrenched silos within social sector organizations, these strategies can help put the people, priorities, and incentives in place to cultivate a data culture of people who responsibly and proactively engage with data to solve problems. The global pandemic has underscored the importance of such strategies: Organizations have needed to adapt to working digitally and remotely, while nonprofits facing existential financial challenges have been forced to use data to invest limited resources where they’re most needed.

However, only 23 percent of nonprofits have a long-term vision for technology, according to a recent survey by Salesforce, and experts we spoke with shared similar observations.

“Organizations are trying to deal first with technology in itself,” said Catalina Escobar, a co-founder of the Colombian social change group MAKAIA. “They’re thinking about starting a digital transformation strategy.”

Some organizations are more advanced. The global NGO CARE developed a Responsible Data Maturity Model to help institutions craft a coherent, long-term data strategy. Partnerships such as the Digital Impact Alliance (DIAL) and New York University’s Open Data Policy Lab help coordinate successful digital and data-transformation strategies in the global development sector through frameworks like the principles for digital development.

If tech companies want their data or computing power to achieve their full potential in the DSSG movement, the experts we interviewed recommended:

• Working with partners that already have a digital transformation strategy.
• Helping organizations to design digital transformation strategies that will enable the meaningful use of data.
• Funding or contributing to efforts, such as those of CARE or DIAL, to support digital transformation at scale.

3. Provide skilled data scientists to work directly with nonprofits.

Tech companies often provide data or infrastructure for nonprofits, but too few nonprofits have the in-house experts (such as data scientists or data product managers) to make use of those digital assets. To fill this gap, some organizations, such as Zindi, put together competitions for online communities of data scientists to solve social problems. And initiatives such as Data Science Africa or Google’s AI Impact Challenge provide funding, training, and data science talent to nonprofits.

Although blueprints exist for tech companies to boost nonprofits’ data science chops, the experts we interviewed had mixed feelings about the success and sustainability of such efforts. The problem is that they can push nonprofits toward depending on tech firms for specific projects for limited periods, rather than building long-term independent capabilities.

“Anything that temporarily increases capacity at a nonprofit is not leading to more data-driven work,” said Claudia Juech, founding CEO and a board member of the Cloudera Foundation. “Once these people pull out, it will be very hard for nonprofits to keep it going and make it a part of their work.”

Some people argue we should experiment with more direct methods to strengthen in-house data capacity in the social sector. But Groups like DataKind have found success with a model they’ve developed for embedding data specialists into nonprofits to solve problems together. Their approach involves two important components: comprehensively understanding a nonprofit’s data problems and, secondly, training data scientists to appreciate the complexities, constraints, and incentives of working effectively inside nonprofits. DataKind’s senior director of product, Caitlin Augustin, and Afua Bruce, the organization’s chief program officer, said it is important to recruit the right people for such projects—they need to be highly skilled but humble, and able to communicate well with non-experts.

Nick Hamlin, a data scientist at Global Giving, agrees with the approach, further suggesting that—given many nonprofits’ struggles with limited resources—the data scientists chosen for such projects possess a “willingness to exist in the presence of uncertainty.”

To help civil society organizations’ address skill gaps in data science and to ensure tech firms’ digital contributions are used to their full potential, our experts suggested:

• Training data scientists, at scale and in a coordinated fashion across companies, on the work cultures, structures, and constraints of nonprofits.
• Continuing the funding of proven models to lend data science talent to nonprofits in longer-term engagements.
• For shorter-term engagements, deploying data scientists who are good communicators and trainers so they leave nonprofit teams with the skills to do the work on their own.

4. Support training for nonprofits to make responsible and ethical decisions based on data.

Social sector leaders need to be mindful of the potential biases that can seep into the collection, analysis, and presentation of data, or they risk taking inappropriate actions based on the information.

There are many educational and training resources tech companies can use to train their own teams and nonprofits in the critical thinking skills that ensure data projects are ethically executed. For example, the Johns Hopkins Center for Government Excellence and New York University’s GovLab provide training on interpreting data or understanding the risks when sharing data. TTC Labs, a project initiated and supported by Facebook, helps people across industries explore how to manage trust and transparency as they work with digital products and data. The global development NGO IREX helps leaders build soft skills for data-informed decision-making , while TechChange’s “Gender Data 101” course helps people be mindful of gender biases when working with data for social impact. To further bolster the creation and sharing of resources like these, our interviewees suggested that tech firms:

• Invest in training and educating their own employees on ethics and decision-making related to data.
• Sponsor training for nonprofits to build critical thinking, interpretation, and decision-making skills.
• Partner with non-traditional learning institutions that build critical thinking skills for using data ethically, and support similar efforts within the formal education system.

Avoid Going It Alone

As tech companies release datasets, donate cloud computing resources, or lend data scientists to the social sector, the experts we spoke with emphasized the importance of couching the efforts within existing systems, strategies, and knowledge. Coordination among all participants in the DSSG ecosystem—government, civil society, and private sector—will be critical, and numerous networks are already set up to do just that: 2030Vision, the Global Partnership for Sustainable Development Data, Data Collaboratives, the Data Culture Project, and others. Working through cross-industry cooperatives like these will help avoid redundant and siloed investments—and ensure we move more quickly toward achieving the SDGs as their deadlines loom.

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SEOUL — The introduction of artificial intelligence and big data technologies will play a positive role In a new normal era as they can be applied to all age groups without alienating old people, who are vulnerable to digital transformation, a private big data center chief said, based on the analysis of consumption patterns.

“In the new normal era, we will evolve into an inclusive society with no digital gap through the use of big data,” Lim Se-hyun, head of the big data center at BC Card, the biggest payment processing company in South Korea, spoke at the Good Growth Global Forum (GGGF) in Seoul on September 9~10.

“According to the recent analysis of consumption patterns, the trend of online replacing offline is accelerating,” Lim said, adding online sales of BC Card clients rose in the first half of this year while offline sales were down. “Consumption patterns are spontaneously and actively adapting to new changes and the new normal era.”

Lim said the perception of home shopping has changed with consumers treating it like friends. “In the case of home entertainment, offline consumption such as movies and performances has decreased, while sales of Netflix and others have increased,” he said.

With the introduction of telecommuting, offline consumption has become pluralized. The center of Seoul, which is densely populated with offices, saw its offline sales drop about 16 percent on-year in the first half of this year, while consumption in residential areas in nearby cities has increased, Lim said.

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Qumulo, the leading cloud file data platform that helps organizations easily create and manage their file data at scale with unrivaled freedom, control and real-time visibility, announced the availability of Qumulo in AWS GovCloud (US) through AWS Marketplace, as well as the upcoming FIPS 140-2 and Common Criteria EAL2+ certifications of its platform.

For U.S. government agencies at the federal, state, and local level, as well as contractors, educational institutions, and other organizations that run sensitive workloads in the cloud, AWS has developed AWS GovCloud (US) to help customers meet FedRAMP, ITAR, or CJIS compliance. Qumulo’s file data platform services now have expanded access in the AWS Marketplace as part of AWS GovCloud (US). Government organizations in the midst of digital transformation can now integrate their file data with both legacy applications in private cloud, and cloud-native applications in AWS GovCloud (US) with a single file data platform provided by Qumulo.

“Government agencies and organizations running mission-critical workloads need the ability to consolidate, analyze, and collaborate on massive unstructured data across on-prem and in the cloud,” said Craig Bumpus, Chief Revenue Officer at Qumulo. “With Qumulo’s file data platform, now available in AWS GovCloud, organizations that require higher compliance baselines such as FedRAMP or CJIS can leverage cloud-native applications and services to provide data-driven outcomes and retain massive quantities of unstructured data securely.”

Today Qumulo serves public sector organizations including:

• National Renewable Energy Labs (NREL) – managing research data for renewable energy scientists.
• University of Florida – enabling the creation, management and sharing of huge data sets, allowing the University to conduct research to find the next answer, the next cure, and the next forward innovation.
• San Diego Supercomputing Center – delivering the compute, storage and data tools to power performance modeling, data mining, and predictive analytics workloads as a managed service for research clients.
• Scientific Computing and Imaging Institute (SCI Institute) – accelerating image analysis and visualization for leading edge medical research.
• Utah’s Weber School District – ensuring student and faculty safety with innovative video surveillance and security solutions.

“Selecting Qumulo takes the guesswork out of our data management and makes us more efficient when scheduling diagnostic operations,” said Erik Deumens, Scientist and Director, University of Florida Information Technology – Research Computing. “We find the Qumulo system to be rich in features and easy to work with. It is a very cost-effective solution so that we are making the best use of university funds.”

The federal “Cloud Smart” policy and impacts of Covid-19 are accelerating the need to manage this file data securely, at scale, and with freedom across clouds. Regardless of whether data is maintained on-prem, in a private cloud, or in the public cloud Qumulo is committed to providing data security as a key element of the file data platform.

In addition to availability in AWS GovCloud (US), Qumulo is currently working with Corsec Security, Inc. to pursue FIPS 140-2 Level 1 validation and Common Criteria EAL2+ certification of Qumulo software on all supported offerings across on-prem and the cloud, making Qumulo the strategic choice for all types of Controlled Unclassified Information (CUI) and unclassified file data.

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