Archive for the ‘Placement’ category

Four Key Priorities for Digital Business Planning

October 15th, 2018

In today’s digital economy, the threat of disruption is ever-present. By now, the stories of new and agile market players breaking through traditional industry boundaries to capture market share is well-known. Airbnb has disrupted the hotel industry, Uber the taxi industry, and Amazon retail.

What all these companies have in common is the effective use of data, which they use to orchestrate experiences that deliver the cost, convenience, and choice customers crave. Importantly, though, this data isn’t used by business units on an ad hoc basis. Quite the opposite. To be successful in the digital economy, companies need to run on data from the ground up, with all lines of business (finance, sales, marketing, operations, supply chain, logistics, and more) sharing data in a comprehensive manner for a common purpose: to deliver better experiences and improved outcomes for customers.

Rethinking Business Planning – A new paradigm

Companies that want to stave off disruption need to think creatively and holistically along the lines of a new paradigm that at SAP we call digital business planning.

Digital business planning seeks to reintegrate supply chain planning and management into the enterprise as a whole so that it is no longer a sub-specialty off to one side of the business. Companies pursuing digital business planning need to think in terms of four key business priorities.

  1. Develop a demand-driven business plan

To survive and thrive in the digital economy, companies should operate according to a demand-driven business plan that shifts the focus from the supply chain to the value chain. The idea is to broaden the planner’s perspective to include all players involved in delivering the final product or service to the customer. Leading companies are moving from manual and sequential planning to automate and synchronized planning where the system uses analytics to resolve problems on its own and machine learning to continuously improve. According to this model, planners are called in to solve problems only in exceptional cases, otherwise, they’re focused on creating value and generating revenue.

  1. Sense, Predict and Respond to change

As companies feel the pressure to move toward faster planning-to-fulfilment cycles, the once separate functions of planning and execution are beginning to blur. To increase agility and responsiveness, companies need a single source of data truth so that all roles can accurately evaluate conditions, simulate the impact of potential actions, and execute decisions in real time. Advanced analytics—with data often fed from embedded sensor data (Internet of Things)—can help not only avoid disruptions in the supply chain but also predict customer behaviour. Many companies are using predictive capabilities to deliver outstanding customer experiences and better outcomes.

  1. Plan holistically across the network

Planning in the digital economy requires an end-to-end approach that widens the lens on the entire value chain. Many companies start internally, integrating planning activities across lines of business. Moving outward, companies then incorporate suppliers (and supplier’s suppliers) in order to collaborate, plan, and deliver more effectively. A flexible supply network platform is also critical, making it easier to discover, onboard, and work with new suppliers to meet constantly evolving and fluctuating demand. Technology leader Microsoft, for example, followed such an approach to dramatically reduce its inventory with a new multi-tiered inventory strategy. Most importantly, leading companies see customers more as integral parts of the value chain, rather than as end points. These companies seek customer input and use advanced analytics to continuously feed it back into the planning process to unlock added value on an ongoing basis.

  1. Increase strategic agility 

Companies across sectors seek the ability to adjust supply chain strategy and portfolio dynamically in response to market opportunities and needs. Leaders on this front have moved to self-regulating, adaptive planning models that help buffer against variability. With live data, real-time analytics, and machine learning tools, for example, companies can optimize planning decisions for higher profitability. Examples include evaluating alternative sourcing and transport strategies to minimize cost and adjusting segmentation profiles to fine-tune inventory levels according to actual demand. With greater visibility into and control over real-time data, companies can now evaluate decisions quickly and drive strategic planning processes that adapt flexibly to shifting demand signals and supply conditions.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Richard Howells.

15 Predictions for the Next Big Thing in Software Development

October 8th, 2018

Technology is evolving faster than ever before. Business owners must be willing to adapt to changes in tech if they want to stay competitive. To do that, though, you must first keep yourself abreast of the latest trends.

We asked the experts of Forbes Technology Council to share what they think will be the next big thing in software development. Now is the time to start looking into these trends so your business can become an early adopter.

  1. Containerization

In many ways,  it is thought that this may already be true for a lot of organizations, but this is just the beginning of a widening trend. The Docker and Kubernetes ecosystem definitely help with moving forward as well, but we expect that within the next few years it will be more uncommon to see teams not using containers. – Zach Bruhnke, Halleman Bradley

  1. Functional Programming

Functional programming is not new, but it has not gained widespread adoption. However, with SPAs becoming more complex, JavaScript is seeing its limitations by making it hard to maintain and debug code. With Elm, Facebook’s ReasonML, more developers are adopting the functional mindset on the client side, and it could possibly be the start of more functional adoption in different parts of the stack. – Salim Madjd, AsthmaMD

  1. Multiplatform UI Development

Many companies end up needing frontends for web, iOS and Android. Doing these three different ways is ineffectual. Several solutions exist to address the last two (e.g., Flutter, React Native, Xamarin), but few handle all three in a reasonably successful fashion. On the back end, server less solutions will be popular for certain use cases. Containers and micro services will proliferate. – Manuel Vellon, Level 11

  1. Native Analytics Modules

More and more products will include business intelligence and analytics modules natively in their solutions, reflecting the need to drive more value from the data these systems generate. Involving development to include natural language generation (NLG) in these BI and analytics modules will become a fundamental requirement as well. – Marc Zionts, Automated Insights

  1. SoftwareTo Build Software

Programming languages have become more and more developer-friendly. There have been initiatives to develop a website or even a mobile app without having any coding knowledge. This would be an important step since it would narrow the gap between imagining a product and creating it in real life. This will make at least showing proof of concept less resource intensive and hence more affordable. – Vikram Joshi, pulsd

  1. Serverless Microservices

The migration from monolithic software stacks to serverless microservices is the path many software companies are taking to better isolate and compartmentalize software development. Breaking apart code in this manner allows small dedicated teams to focus exclusively on specific areas with minimal impact on the whole. Many large companies have already achieved this. The rest of us should follow. – Chris Kirby Retired

  1. Data-Driven Rating Systems

We are seeing a collapse in the credibility of online reviews and ratings that are generated by humans. Software platforms that generate objective ratings for products and services, based on an analysis of actual prior usage data, are going to be critical in enabling better decision-making. – Daniel Levitt, Bioz

  1. AI-First SoftwareDevelopment

AI and machine learning-driven product features are already an integrated part of software development for e-commerce, movie watching and social media. Now AI-first software, from conversational virtual assistants to self-driving technologies, are becoming mainstream in software development. – Mitul Tiwari, Passage AI

  1. Earlier And More Frequent Security Testing

The inevitable evolution of DevOps will be to include security testing earlier and at more points in the development pipeline. Security testing is currently a bottleneck for delivery, and the cost is highest to remediate code when done late in the cycle. Providing developers with real-time feedback on the security of the code they are writing is the ultimate goal to avoid delays and expenses. – Travis Greene, Micro Focus

  1. Human Behaviour Modeling

The next trend will be programming human behaviour — creating computational models of human behaviour and developing algorithms to aid the customers/users with possibilities and choices. Finding the trends using digital behaviour can calculate the next move of the user. Programming perceptual processes will be the next big thing in software development, and it will help mediate digital identity and behaviour. – Komal Goyal, 6e Technologies

  1. Increased Third-Party API Integrations

We see a rising trend of customers choosing to use external API instead of custom development. It takes less time for development and helps save money at the beginning. In a few years, developers will be working mostly on integration between different services instead of developing a custom software solution. – Ivan Verkalets, COAX Software

  1. Edge Computing For Data Processing

Edge and fog computing will change how we process data. We’ll see a higher degree of computing happening at initial data capture to remove processing workload from the server side. This is essentially what’s already happening with IoT; however, in the future, we’ll see this in other non-IoT uses cases as well, like ensuring financial compliance locally instead of in a central data centre. – Claus Jepsen, Unit4

  1. Graphing Tools To Illustrate How Systems Work Together

As the world and our devices become further intertwined, the behaviours or rules within software systems become increasingly complex. Businesses need graphing tools where engineers illustrate how these systems are working or not working together. – Larry Lafferty, Veloxiti Inc.

  1. Blockchain

Blockchain technology holds incredible potential for many industries, especially when used in tandem with internet-of-things (IoT) data, artificial intelligence (AI) and fog computing. Software developers will be focused on building disruptive, new solutions that leverage blockchain ledgers such as solutions to enable micropayments and smart contracts or end counterfeiting in the supply chain. – Maciej Kranz, Cisco Systems

  1. Continuous Evolution

Continuous evolution involves your team’s ability to learn while they produce. You cannot expect everyone to be perfect on Day 1 or Month 1. The question is, how can you utilize the now and here with talented people? At what point are they ready to go? Enter continuous evolution. The knowledge and quality of your team will eventually converge into a stable line of progression. Now you can evolve. – Waije Coler, InfoPay

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Forbes Technology Council.



Corning Moves HR to the Cloud: Employees Say This Is Amazing

October 1st, 2018

Moving human resources (HR) to the cloud was the best way for Corning to give employees the same experience as its customers. This leading global manufacturer of specialty glass and ceramics has about 50,000 employees in 35 countries. Its goal was to modernize HR to attract and retain the top talent needed for continuous growth and innovation.

“We wanted to bring into the workplace the same customer experience our employees have in their personal lives,” said Christy Pambianchi, Senior Vice President of Human Resources at Corning. “People evaluate companies based on the kind of tools and work environment they utilize. We also knew our ability to rapidly assemble and connect employees worldwide was going to be critical to our competitive advantage.”

We wanted to bring into the workplace the same customer experience our employees have in their personal lives @successfactors @corning

Christy Pambianchi, SVP, Human Resources, Corning

No training needed

Unlike past HR projects at Corning, the move to SAP SuccessFactors didn’t require a huge investment in training materials. While Corning shifted all HR processes ─ recruiting, payroll, performance reviews ─ to the cloud, people had no trouble learning how to use the highly intuitive software.

“We’re getting thank-you emails from employees saying this is amazing, I’m a millennial, I love this product, I feel like you brought this company into the 21st century,” she said. “Within a month we had about half a million hits on our talent acquisition website.”

Cloud builds people community

According to Pambianchi, SAP SuccessFactors makes it easy for employees to find each other and work together, as well as stay connected to the company. Workers shared some of the loudest praise for mobile access to the HR system.

“Employees love that in their pocket on their mobile phone or tablet, they can look at their employee information and change their address, look up available jobs, and find anyone in the company,” she said. “All of a sudden our company has this community in the cloud where people can find each other and work together in a way that wasn’t possible before.”

The benefits haven’t been limited to traditional, office employees. Pambianchi said that factory workers are just as excited about having access to HR information through the cloud-based system.

Co-innovation partnership

Corning, which also uses SAP Fieldglass, SAP Concur and SAP Ariba, and other SAP solutions, selected SAP SuccessFactors as part of a long-term co-innovation partnership.

“I do HR for a living. I don’t necessarily think about how to invent the next HR technology,” said Pambianchi. “Partnering with SAP makes me feel like I have an R&D department working on the best things for keeping people and HR capabilities at the forefront of what’s happening technologically. Whether full-time, part-time or temporary, we want all employees to have the same knowledge, skills, and cultural experience as every employee.”

She added that the recent release of GDPR-compliant capabilities in SAP SuccessFactors was a prime example of the benefits of being in partnership with SAP.

Next up for HR at Corning is taking advantage of AI and machine learning. Pambianchi said the team also looked forward to transforming learning delivery, getting content to people faster and driving talent as a competitive advantage.

This blog also appeared on the SAP News Center.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Susan Galer.

The Rise of Consumer-Centric Marketing: Made Possible Through Data Science

September 24th, 2018

Traditional business models — those based on mass advertising and comparatively impersonal marketing — are rapidly being turned on their heads as brands instead focus on forming individual connections to consumers themselves. These brands are modern, nimble and naturally tech-savvy, and they’re bringing a digital and data-driven business model to the table in order to become the driving force behind a new, consumer-centric economy.
Indeed, the rise and influence of direct-to-consumer (DTC) relationships should be well-noted. Big brands that fail to effectively engage their consumers are at risk of becoming obsolete. However, in the era of the omnichannel, how do you effectively reach consumers in a way that’s optimized across each and every channel?
Given the rise of mobile-first economies, marketers in any industry will tell you the answer is data. However, data by itself is useless if not made actionable, and brands like Marks & Spencer have figured out one solution by training employees to become well-versed in the language of analytics and data science. Improving data literacy throughout your organization helps enable marketers to map consumer preferences, concerns and buying habits at scale.
Modern marketing in a digital-first economy takes a multifaceted approach, but at its core, it is driven by data-backed decisions. The source of this data also varies widely given the rapid evolution of the consumer journey and the number of touch points along the way.
Having helped craft data-driven marketing strategies for more than a decade, I’ve seen firsthand that in order to effectively wrangle and consolidate across all touch points, as well as make correlations regarding consumer trends, the marriage between marketing and data science is more important than ever.
Rethinking Consumer Journey Mapping
Consumer-centric campaigns by their very nature require different marketing approaches. And engaging modern consumers is, in any case, a mix of science and art: the creative of marketing combined with the analytical power of data science. Marketers want to understand how to measure the consumer journey and find levers like personalized marketing to influence the path. The problem is that the consumer journey is complex, and the typical media mix models have long delays in analyzing consumer behaviour. Real-time action ability is a challenge, as models that don’t get retrained with new signals lose efficacy. This is where data science comes in.
Data orchestration to create actionable insights and applications is the newest piece of the puzzle and will come to be the norm. Increasingly we are also seeing identity services offered in programmatic, such as through The Trade Desk and Sizmek. This is because identity can prepare consumer journey mapping data, while the data science team assists with executing the planned strategy. This then enables marketers to personalize messaging and improve consumer engagement in real time, all while leveraging the creative of marketing to engage consumers effectively.
Merging marketing with data science ensures real-time signals are captured to allow for rapid testing so models can be optimized accordingly. The process, in turn, facilitates a better understanding of the consumer journey across all channels as well as a data-driven methodology to improve engagement.
One of the challenges in many organizations is that data scientists still spend 80% of their time accessing data, and only 20% of their time on data analysis and collaboration. With the right tooling, organizations can reduce manual tasks and optimize workflows.
Overall, it’s not just about the platform or tools, but it begins with the process. In order to get started, bring the data science and marketing teams together for rapid testing and learning. This means you need three things: tooling to bridge data and workflow between data and creative teams, the infrastructure to run tests and analyze them rapidly (and, ideally, automatically), and finally, a big enough user base that your tests are powered through appropriately, even over a short period of time.
Direct-To-Consumer: Wave Of The Future?
DTC brands have set the precedent and are thriving, but marketers within any industry — from start-ups to Fortune 500 organizations — will reap the benefits of establishing a “data economy.” Thinking from the perspective of a data scientist and data-first approach will also allow brand marketers to detect anomalies in their overall marketing strategies, anticipate hurdles and adjust accordingly.
During the initial rapid test phase, be curious and explore different options, paths and possibilities. With the right tools and strategies, continuous improvement becomes the norm, enabling marketers to approach the challenge from multiple angles and to test and weed out what does and doesn’t work in real time.
With two-thirds of all U.S. consumers expecting direct connectivity to brands, capitalizing on a consumer-first approach presents a significant opportunity for the modern marketer to integrate data science. Brands can ultimately achieve greater results through optimizing their traditional marketing stack by not only leveraging the power of data but understanding its full effects and potential implications.
Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Forbes Agency Council.

SAP SuccessFactors Gets Human over Digital HR

September 17th, 2018

Automation technologies are the current darlings of the tech zone. Driven by Machine Learning (ML)-powered Artificial Intelligence (AI) and big data analytics, if a process task or service can be defined, mapped, compartmentalized and defined as a discrete component, then in theory it can be automated by software.

So if we can digitize every aspect of business and life, then why not digitize us ourselves as humans? Okay, perhaps not our living tissue as such (although fully blown implanted bio-intelligence will surely be next), but our human needs in the workplace… that thing we call Human Resources (HR) or Human Capital Management (HCM).

Digitized HR conundrum

But as we automate and digitize into HR, which way does the balance shift — that is, does digital Human Resources force firms to become more digital, or does it in fact give them the opportunity to become more human?

German data software company SAP bought SuccessFactors at the back end of 2011 and has kept the company name to describe its HR applications and tools division. With new forays into Customer Relationship Management (CRM) and a history steeped in Enterprise Resource Planning (ERP) software, bringing the ‘personnel’ software factor into its stack at the start of this decade was a logical enough thing to do.

Given the opportunity to now digitally capture and empower many more staff actions in the workplace, SAP’s wider play is one that sees employee information channelled into a total proposition that it likes to brand as the Intelligent Enterprise (CAPS deliberate). The firm insists that it can make digital HR a more human-focused thing and it has recently expanded its SAP SuccessFactors software toolset with that specific strategic aim in mind.

Digital HR help for humans

The latest product developments from the SAP SuccessFactors camp now see the firm offering a new digital HR assistant. Currently in beta (pre-launch) form with a number of test-case customers, this is software designed to guide and recommend worker actions based on verbal and/or written questions or commands. SAP makes much of the Machine Learning (ML) element in its SAP Leonardo ‘design thinking’ brand and ML is highlighted here as a key function to allow the software to ‘learn’ more about what kinds of HR requests a user might make as it goes along.

This new digital assistant is built using the SAP Co-pilot bot framework and SAP Leonardo machine learning to create a conversational experience. It is also integrated with collaboration platforms including Slack and Microsoft Teams.

According to Andrea Waisgluss, user experience content strategist for SAP SE, users can chat asks questions and give commands to these chatbots just as they would a regular person. “The user’s informal and unstructured speech [is] then contextualized, analyzed and used to execute actions and present the user with business objects, options and other relevant data in a simple and conversational way,” she said.

Talent metrics – a means to measure humans

In terms of background intelligence to drive this new app and to help direct the machine learning engine in the new digital assistant, SAP SuccessFactors global head of marketing Kirsten Allegri Williams has explained that SAP has a catalogue of more than 2,000 ‘talent metrics’ (along with guidance on how to interpret the information) as the basis to accelerating the analysis of workforce and business issues.

“[These metrics include data focused on areas including] hire and hiring, learning, mobility through the organization and ‘span of control’, demographics and diversity, absence, performance, payroll, compensation, career paths, leadership and succession, through to retention and turnover… and ultimately, metrics related to business outcomes in terms of growth, revenue and profitability,” said Allegri Williams.

SAP CEO Bill McDermott has said that back when he was a teenager running a corner deli store, his CRM system used to be the front window pane and his HCM system was a hug [from a happy customer]. McDermott has also said that it’s really important that enterprises do not run their businesses based on the dissemination of emails to stipulate adherence to Key Performance Indicators (KPIs).

“Somehow we have to make these big companies feel like small companies again,” said McDermott.

Of course, there is another conundrum here. SAP makes the bulk of its money selling business analytics software that helps customers track what’s happening in their operational models down to a fine degree. We can perhaps safely assume that he would suggest we work with a reasonable mix of both humanity and digitization.

Ultimately, actually, digital HR might actually be a necessity. SAP SuccessFactors president Greg Tomb has noted that as much as 44% of company workforce spend today is channelled towards and spent on external workforce elements. Tomb also notes that the workforce is no longer a narrowly defined group of people. For most organizations, the workforce is a diverse, globally dispersed, mobile collection of individuals who are often disengaged from the enterprises they work for.

As part of extended product news, SAP has announced the creation of a new ‘open community’ intended to create purpose-built HR applications. The company hopes that small start-ups and larger established enterprises will come together to ‘co-create’ what could be large-scale applications or smaller ‘micro-apps’ (pieces of software with more limited specific functions) based around six initial pillars The new community is organised around apps that fall into six initial pillars: well-being; pay equity; real-time feedback; unbiased recruiting; predictive performance; and internal mobility.

“We believe this wave of innovation will result in a ‘human revolution’ that will allow businesses to focus time, talent and energy on the thing that really matters: the people that lead to business outcomes. With this community, we can help assemble a complementary set of solutions for our customers’ diverse needs. And, if they don’t exist yet, we can co-create them together,” said Tomb.

Digital HR humanity

So is there are a real difference between old school HR and new age digital HCM – and, back to our original question, does digital Human Resources negatively force firms to be more digital, or in fact allow them to become more human?

The answer lies in the fact that digital HR ‘should’ make companies more human if it is embraced and implemented correctly in a holistically connected way with multiple channels of access. If we apply it carefully, digital HR can help us identify bias, inequality in the workplace and also help us focus on human well-being, because we’ll know more about what people are actually doing in the roles they are assigned to.

Humans are obviously an integral part of so-called digital transformation on the road to cloud, web-scale business and ubiquitous connectivity, let’s just hope we can keep the human factor on the upper surface as we go forward.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Adrian Bridgwater.

Can Big Data Alone Keep Up With Ad Tech?

September 10th, 2018

Ad tech is unique, with its own distinct requirements and constraints. Digital advertising is becoming increasingly transacted via programmatic means, which demands technology that can not only accommodate extreme data volumes, but that can also process the data at the pace of real-time digital business. The question is, can ‘big data’ alone suffice for all the needs of the ad tech industry?

The Holy Grail of digital advertising is to reach the right consumer, with the right message, at the right time and in the right place. Keeping track of return on investment of marketing budgets with the right attribution is also very important. The challenge is to identify the right technology to mine the data and efficiently process it into a sell-able asset; it is the refining process that makes the raw data valuable.

At PubMatic, we understand that value lies in the quality of the data refinement. We are committed to providing high-quality reporting and analytics to empower our clients to leverage data at every stage of a campaign to inform programmatic activity and make smarter, faster business decisions. In building our platform, we defined three areas where we had to perform.

  1. Volume of Data
  2. Instant Decisioning
  3. Manageable Cost

Volume of Data

Successful customer engagement in the ad tech space demands lightning-fast queries on high volumes of complex data. We must be able to accommodate larger data sets and deliver more complex deals and services to largest clients. The deployment must be flexible enough to provide cost-effective and easy-to-consume services. We want to give our clients the ability to translate massive volumes of complex data into digital insight at unparalleled speed, with streaming data analysis and streamlined machine learning. To do this, we augmented our Big Data platform with a new class of technology focused on accelerated parallel computing. With Kinetica, a Graphical Processing Unit (GPU)-powered database that contributes high-speed data processing capabilities, PubMatic can empower our customers with real-time reporting and a sophisticated ad pacing engine.

Instant Decisioning

Advertising is the lifeblood of the internet, and digital advertising is increasingly transacted online programmatically, with eMarketer estimating that over 80% of digital display ads will be bought programmatically this year. Programmatic buying and selling of advertising uses real-time bidding to match marketers, who are trying to reach consumers across desktop, mobile, and over-the-top devices, with publishers and media companies that attract people with content. Digital advertising demand-side platforms (DSPs), sell-side platforms (SSPs), and centralized data management platforms (DMPs) and exchanges are dealing with a fire hose of real-time data that needs quick analysis to make advertising tick. At PubMatic, we needed to be able to sweep through vast volumes of complex streaming data in milliseconds, in order to create, target, and deliver ads with incredible speed and our signature precision. Technology-wise, we rely on the speed and parallel-processing power of Kinetica’s GPU engine to get the job done. Artificial intelligence powered by GPUs can optimize auctioning by discovering patterns and uncovering hidden insights in sub-seconds. By running ad decisioning algorithms, it’s easier for us to target the right audience and display the ads likeliest to appeal to them.

Manageable Cost

Programmatic trading operates at significant scale, with PubMatic generating over 400 terabytes of uncompressed data each day and processing over 10 trillion advertiser bids per month. However, the value of each individual transaction is relatively low compared with other industries. Therefore, the cost per transaction must be lower than many other industries; that means the infrastructure footprint has to be smaller. The ad tech industry leads in defining next-generation data platforms that can handle huge data sets with lower cost per byte requirements. We’re confident that adopting our technology criteria can only positively impact everyone’s bottom lines.

The key to success is getting the right tool for the problem. Digital advertising operates in the realm of extreme data, where it’s all about volume, speed, and cost. The increase in data volume is unpredictable but the costs can’t be. While it is easy to get stuck with familiar technologies, big data alone is not enough to keep up with the pace of ad tech.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Vasu Cherlopalle.

Data Retention: Tough Choices Ahead

September 3rd, 2018

As the cost per byte of storage has declined, it has become a habit to simply store data “just in case.” At a time when the overwhelming majority of data was generated by human beings, nobody thought much of it. Data was summarized, information extracted from it and the raw data points were still kept should they be needed later. Later seldom came.

Cisco tells us that as of 2008, there were more things connected to the internet than people, so we can use that as the point in time when the amount of data being generated and stored had its hockey stick moment. Now we have more sensors in more places monitoring more and more activity and generating more and more data points. By 2010, then Google CEO Eric Schmidt explained how we generated and stored as much data every two days as we did from the dawn of civilization up to 2003.

That’s a lot of data.

Running Out Of Room, 0r…

The natural reaction is to instinctively feel that, at some point, we’re going to run out of storage capacity. If Moore’s Law holds, that won’t happen. We’ll just keep inventing new, more compressed storage technologies.

But what we are running out of is time.

Long ago, the last thing anyone in the data centre did was to make sure the daily backups were running. They would run into the night all by themselves. Then they would run through the night. Then they were still running when everyone came into the office in the morning.

Fortunately, we’re clever and adaptable, so we came up with incremental backups. Instead of recopying and recopying data we had already copied, we only copied data that had changed since the last backup. Then we moved to faster backup media. Now we’re backing up the data as we’re saving it in primary storage. Ultimately, the restore time objective becomes impossible to achieve in the time available to us.

Making Tough Choices

Now we have to make a difficult choice. Once we’ve processed the data and created valuable information, do we or do we not keep the original raw data as it was collected? Or do we decide to discard it?

Or do we have to choose to save some of the raw data and not other parts of it? What are the criteria upon which that choice can be made? How do we anticipate in our planning which data points need to be stored and which will be discarded?

Now Add Machine Learning

This problem becomes exacerbated by the introduction of machine learning and artificial intelligence technologies to data analytics. When a machine is performing much of the data collation, selection and processing, how are we to know which data points the machine will want to retrieve to complete its analysis? What if we choose incorrectly?

Other Possible Strategies

Being more pragmatic about this challenge, we need to think about data reduction. First of all, when and where does it occur?

Many of us take a physical relocation from one place to another as an opportunity to discard belongings that we no longer need. Some perform this discarding as they are packing to move. Others, often in a rush to make the move, simply pack everything and promise to do the discarding when they arrive at the new location. Many of us have boxes upon boxes that have yet to be unpacked since we moved in many years ago.

In the classic framework, we can choose to perform data reduction at the core of the network, in the server processors that will perform all the analytics. Or we can choose to perform data reduction at the edge where the data is being collected so the load on the servers and storage are reduced.

It is likely that the ultimate solution will be a combination of both, depending on the workload and the processing required.

Begin With The End In Mind

There has been much discussion about data science — how it’s the art of extrapolating useful information from data and turning it into knowledge that facilitates superior decision making.

As we continue to see the internet of things produce Schmidt’s estimate of five Exabyte’s per day, data science must expand its scope to include the development of an end-to-end data strategy. This must begin with careful planning and consideration surrounding the collection of data, layers of summarization and reduction, pre-processing and, finally, deciding which data points get stored and which are discarded.

As always is the case with data storage issues, this will be a volume-velocity-value process based on the business use case involved and at what point data gains value. The science is nascent, but the opportunity is immense.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Rick Braddy.

10 Ways To Get Different Teams To Work Together When Creating Software

August 27th, 2018

When thinking about developing software, usually the programmers are immediately the people who come to mind. However, it takes more than programmers to get a piece of software to market. In fact, a marketing team is an essential part of the process. However, they’re often either kept in the dark or brought in a little bit too late. There is a way to fix that.

It’s important that marketing professionals and programmers work on a project together to ensure success. We asked members of the Forbes Technology Council how to handle two different teams working together on the same project. The advice given offers a few different ways to bring the two teams together to execute a project successfully.

  1. Establish Leadership And Culture

By establishing a culture of collaboration and consistent communication, companies place themselves in more optimal positions to have inter-collaborative departments. A culture of collaboration and teamwork starts with the leadership and the company culture. That then sets a precedent for the rest of the organization. Making sure those in leadership positions from both teams have regular meetings is a good idea to keep both teams in the know. – Alexandro Pando, Xyrupt

  1. Create Cross-Departmental Teams

Communication between two teams is crucial. Having a marketing person in development projects (e.g., product manager) or having a technical team member lead part of the marketing team helps when they are defining use cases. This approach lets both teams have a common understanding of capabilities from the beginning ensures both take ownership. – Viren Gupta, Eponym

  1. Let The Primary Team Lead

For market-driven projects, start with the marketing view and align it to the execution. Translate the press release to a rationale/overview, customers to personas and features to use cases to guide the engineering team. For technology-driven projects, reverse the process and extract out details for external communication, both content and target audience, to equip marketing. A skilled product manager is key to these successful “translations.” – Ketaki Rao, Jivox

  1. Add Marketing To The SDLC

The software development lifecycle (SDLC) usually entails some version of research, design, development, and testing and user acceptance. However, if you introduce marketing alongside the SDLC, it becomes part of the process. – Daniel Hindi, BuildFire

  1. Use Product Managers As Liaisons

Interrupting programmers is costly, and it’s not always reasonable to expect that they will understand the business upon which their code runs. That said, one of the great values of product management is that they are often the glue that sticks the product together, interfacing with all parts of the company. Leverage your product managers to translate between engineering and marketing. -David Murray,

  1. Schedule Huddles

We have marketing and programmers conduct quick huddle meetings to go over what is being developed and why it is being developed. Developers get answers to their questions on customer usage scenarios and product positioning in the market. Marketing people get understanding on technical key points, which they can use to sharpen their market positioning. Huddles are effective when conducted once or twice a week. – Mandar Bhagwat, SpadeWorx Software Services

  1. Leverage Processes And Rules

We have clear processes and rules for every project that gets to our Kanban board. We strictly follow the agile principles in our workflow. It helps our marketing and development teams prioritize the projects they work on together, communicate easily and deliver quality solutions on time. – Ivailo Nikolov, SiteGround

  1. Complete A Market Requirements Document

If you wait until the product is complete, you are way too late to effectively market it. Collaboration at an early stage is best accomplished by the technical and marketing leaders co-authoring a complete market requirements document (MRD). A codified, tangible document eliminates the guesswork of who agrees to what and sets the proper prioritizations in stone for the whole company to see. –   Billy Bosworth, DataStax

  1. Think Outside The Stereotypes

It’s easy to stereotype programmers as “resistant to change” and those from a marketing department as too “free-thinking” to understand tech limitations. However, both teams generally have the same goal in mind: growth and company success. Try letting the two teams meet outside of the confines of these workplace stereotypes (e.g., an after-hours mixer). You may find that common ground is met and issues get resolved. – Jason Gill, Attracta

  1. Assign A Chief Visionary Officer

Every company needs a founder who accepts an ancillary role. The role of CVO (chief visionary officer) helps employees focus on a common mission. Once all team members believe in a common vision, the rest is relatively easy. The common vision can then be translated into an executable strategy by breaking down each milestone into smaller tactical steps that can be clearly understood and followed. – Karin Lachmi, Bioz

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Forbes Technology Council. 


Six Smart Ways You Can Use Big Data To Strengthen Your Leadership

August 21st, 2018

Big data is used by companies around the world to inform and improve countless business processes, from customer service to marketing campaigns. But the ability to collect and analyze vast amounts of information isn’t just useful for external operations; it can help you strengthen your business internally, too.

One often-overlooked application of big data is leadership improvement. By looking at a variety of data points like performance metrics and employee survey results, you can determine what’s working and what’s not, and ultimately strengthen your leadership abilities. Below, six members of Forbes Coaches Council explain how.

  1. Reducing Guesswork For More Targeted Decisions

While data can be imperfect, it can generally help identify trends, and from those gaps, development or hiring practices can evolve. Less guesswork can lead to more resources spent on ways to enhance leader’s capabilities. That can lead to stronger teams, happier customers and better ROI. And leaders who lead well and employees who will enjoy working for them. – Kari Price, The Art of Being a BOSS

  1. Customizing Leadership Criteria To Your Specific Context

Much leadership advice falls short because it is generic. Big data can help you customize what it takes to excel in your context, company, industry and culture. For example, what are the attributes of the best managers at the firm? In financial services, we use big data to get rid of the false dichotomy between producing revenues and managing people. – Shoma Chatterjee, ghSMART

  1. Identifying Common Gaps

The more data we can access, the better we can assess the most common pitfalls of aspiring leaders. As we gain this information, we can tailor trainings to help leaders develop skills early in their academic or work careers that will counter these common gaps. – Billy Williams, Archegos

  1. Instructing And Creating Dialogue With Your Teams

What the online universities and other remote-focused institutions know is that you need to bring big data into your virtual classrooms. Don’t firehouse big data at employees; use big data to teach. Educate, interact and ask for insight into the numbers. Leaders should share what the data seems to say. Get their insight, and integrate the human element as a leader. – John M. O’Connor, Career Pro Inc.

  1. Pinpointing Where To Invest Your Team’s Resources

Big data provides insight into areas that need attention and allows leaders to make decisions based on evidence. Companies that make data-driven decisions perform better overall. Data should be used to pinpoint where to invest budget and time to increase efforts, but it is not a replacement for having and communicating vision and setting goals. Big data should inform leadership, not replace it. – Jean Ali Muhlbauer, People at Work

  1. Evaluating Employee Perspectives On Leaders

When fear is present during communication, truth cannot be exchanged. Source your big data in a way that allows contributors to be completely honest about their perspective on a particular leader. Singular input is key, as one bad managerial experience could easily taint one’s view of leadership as a whole. If successful, you’ll end up with better leaders and better people. – Derrick Bass, Clarity Provoked

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Forbes Coaches Council.



14 Ways To Protect Your Key Data (Without Buying More Software)

August 13th, 2018

Data security is one of the top priorities — and biggest challenges — for modern businesses. High-profile breaches and data leaks are happening constantly, and companies need to remain on alert to ensure their sensitive information doesn’t fall into the wrong hands.

For smaller businesses on a budget, it might not be possible to invest in all the latest tools and services that larger enterprises use to protect their data. Fortunately, there are low-cost strategies and processes you can implement to boost your security internally. Here’s what 14 members of Forbes Technology Council recommend doing to guard your company’s most critical information.

  1. Keep It Simple

While some people point to training and education as the primary approach to more secure processes, the reality is that the simplification of processes does far more to ensure security than any education. Complexity is the enemy of security and availability. – Danny Allan, Veeam Software

  1. Plan Ahead

By planning ahead and accounting for privacy settings from the beginning, companies will be better prepared to protect key information. Additionally, your company should establish data security requirements throughout your organization for the full business process. – Alexandro Pando, Xyrupt

  1. Encrypt Everything And Run Penetration Testing

Encrypt everything with industry-grade security configurations, but know that attempts to breach your data are more and more likely to occur as your company grows. The best way to stay ahead of this curve is to hire a third party to “white hack” by doing penetration testing and seeing where they can exploit your company. If they do it first, you’ll prevent the black hackers from doing it later. – David Murray,

  1. Enable All Optional Security Features

With services such as Salesforce, Microsoft Azure and G-Suite, companies should pay specific attention to optional feature-sets. Multi-factor authentication, data encryption, and validation rules are all free features you can enable to help secure access and data storage. Today’s cloud services make security very easy, but your deployment needs to be investigated to ensure you’re making use of it. – Tom Roberto, Core Technology Solutions

  1. Focus On Employee Education

Employee education is a huge part of this puzzle that is often overlooked. All the money invested in data privacy technology can go up in smoke the moment one of your employees makes a wrong move with PHI or PII — sharing it inadvertently or not using a privacy filter screen on an airplane. Require all employees to complete training annually and make data protection part of your culture. – Kevin McCarty, West Monroe Partners

  1. Use Open Source Solutions And Invest In Human Resources

To protect key information, companies can implement various open source solutions in their on-premise or in-cloud infrastructure. They are free of charge; however, a sufficient investment in human resources is needed so you can form a knowledgeable team that can build proper intrusion detection, intrusion prevention systems and adopt the best security practices. – Ivailo Nikolov, SiteGround

  1. Add A ‘Canary In The Coal Mine’

Your existing firewall or IDS software already has the ability to create logs when certain strings are found in network packets. Create test accounts with unique names and details in your system, and then have your network team set up rules to alert you when that information passes through the firewall. This simple step can give you an immediate notification of any unusual data exfiltration or breach. – Jason Gill, Attracta

  1. Use Existing Resources To Their Full Extent

When you are on a budget and need to protect your organization’s data and privacy, try to use existing resources to their full extent. Teach employees how to identify phishing emails, disable Microsoft Word’s macro, double-check the browser’s address bar before entering information, etc. You should also assign IT staff to review existing security software/hardware. – Song Li, NewSky Security Solutions

  1. Leverage Your Cloud

Building up security infrastructure isn’t easy. By leveraging the cloud and enterprise solutions, you shift the burden of technical security to outside partners who have proven abilities to secure their partner’s data. Additionally, having a proven vetting process for your vendors that is documented will mitigate claims of negligence. Lastly, don’t hold what you don’t use. – Kyle Pretsch, Lucky Brand Jeans

  1. Establish A Threat Response Plan And Team

Establish a thorough threat response plan and dedicated team. Routinely test them and ensure you’re also challenging existing cyber defences with penetration testing on at least an annual basis. You should also be doing inventory spot tests across your organization to ensure no personal data lies hidden or untracked. – Ryan Kearny, F5 Networks

  1. Start With Ethical Data Practices

Start with having full awareness of software already in use — where data is and how it is protected. Follow with regular data security and privacy reviews and live scenario training. Adjust or rebuild architecture to support enhanced data compliance. Create a cyber security culture that sticks. All this intrinsically leads to more effective and ethical day-to-day activities for everyone. – Timo Rein,

  1. Only Store What You Need

Companies spend a lot of time seeking and storing a whole lot of information that is not required. It is very important to compartmentalize the data, storing the absolute minimum amount of data required to run the business. Convert account numbers into tokens at the first available opportunity. – Mahesh Vinayagam, qBotica

  1. Solidify Processes Around Data Access, Changes, Audits And Sharing

Analyze your data inventory and establish a tight process with data access. Scrub confidential information before you share data and enforce a tight change management process. In addition, you should audit key vulnerabilities with static and dynamic scans using open source code analyzer. Finally, you should review and secure your data centre access points and ensure that all data is encrypted. – Amit Mondal, PowerSchool

  1. Enforce Good Standards Across The Company

The best prevention without any external software is enforcing password policies through guidelines, how-tos, and best practices about password creation, cookie management and two-factor authentication. By enforcing these, you can prevent the “weakest link” so to speak from becoming an entry point that hackers can exploit to bring down your entire system. – Anand Sampat, Datmo


Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Forbes Technology Council.