Archive for the ‘Placement’ category

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.

11 Signs Your Software Is Due For A Major Update

August 6th, 2018

In April 2018, Google announced a significant Gmail update that included several new features and a visual redesign. This revamp is the biggest change to the popular email service in years, which serves as a reminder that every software company — even giants like Google — need to rethink their approach every now and then.

But how do you know when it’s time to make a major update? You shouldn’t redesign your software just for the sake of change, but there are plenty of legitimate reasons to consider a big upgrade. Eleven experts from Forbes Technology Council shared some telltale signs that your software is in need of a change.

  1. Your Customer’s Opinion Of You Has Become Sour Or Stale

Carefully listening to your customers can signal when a revamp is needed. If there’s a large number of customer complaints or issues that are hurting your brand/product, or if customer growth has stalled and flat lined, a revamp could help re-energize it. Beware that with any substantial change, some customers will be lost. However, a well-calculated one will likely bring new success. – Peter Kuang, Saatchi & Saatchi Wellness

  1. Your Software Is No Longer Helping You Achieve Your Goals

Major updates can be a double-edged sword but are necessary to keep your business up to date and applicable to new advancements. Adaptation and flexibility are a must in today’s tech atmosphere, especially to survive major updates. Due diligence, attentiveness to the existing landscape and clear goals should be the centre of the update. – Alexandro Pando, Xyrupt

  1. There’s A Security Or Functional Optimization To Be Made

As a cyber security software provider, our product features from security to the end user and admin experience are under constant review and improvement. Internal infrastructure and quality assurance teams also do a great job toward further optimization. We provide our customers with newer versions at least four times a year so they get the latest and best security and user experience functions. – Bojan Simic, HYPR Corp.

  1. It’s Been A Few Years Since Your Last ‘Big’ Update

The principle of agile software development is to have smaller updates, more often. However, sometimes there is an idea that can’t be finished in a two-week sprint. That’s when we have a major update coming. The problem is that clients are used to getting major updates every two to three years, so even if there is no major update coming, software providers should freshen up the UI with a redesign. – Dzenis Softic, Clickbooth

  1. Customers Are Requesting Capabilities That Aren’t Possible In Your Existing System

It’s critical to be in touch with customers. If your architecture or code structure has reached its limits, you’ll see an increase in requests for capabilities that aren’t possible in your existing system. For critical areas, it may be easier to do a one-time change and explain it clearly to users instead of incrementally changing the product and potentially upsetting customers on a regular basis. – Bob Muglia, Snowflake Computing

  1. Your Competitors Are Making Major Changes To Suit Customer Demands

In the software as a service (SaaS) market these days, customers know exactly what they are looking for from software. Listening to them will give you insights not just about what is working with your software and what needs a change, but you will get an in-depth understanding of the competitive landscape and what your competition is up to. – Kumar Erramilli, ACTO App

  1. Your Customers And Front Line Staff Have Brought Up Issues

It’s easy to get Stockholm syndrome with software that you’ve developed, leading to an aversion to change or criticism as interfaces become outdated. To prevent this, regularly survey your customers and your front-line staff that use your tools on a daily basis. This can expose pain points and issues you’ve become blind to. Don’t forget to use your competition’s interface as a guide as well. – Jason Gill, The HOTH

  1. You See The Potential To Improve The User Experience

Your software is always due for an update. Nothing works perfectly as designed, and great technology companies are always pushing the limits of what creates a better customer experience. Of course, change for its own sake is distracting and frustrating for users, so before making any changes, you need to believe deeply that the change you’re considering significantly improves your user experience. – Kieran Snyder, Textio

  1. Your Product And Growth Metrics Are Slumping

Always keep an eye on both product and growth metrics. Product metrics such as retention or a specific conversion funnel helps to be sure the products sticky and people are using it. Growth metrics such as daily active users (DAU) or revenue show how’s it performing. A major update would be a result of an observation based on those metrics. – Andrew Neverov, Trucker Path

  1. The User Community Is Creating Hacks And Mods

A functional and mature software package will have an extended life in the marketplace. Things like Gmail and Outlook are fundamental for organizations. When the user community begins implementing mods, hacks or advanced extensions for your platform, it typically means the user base is unhappy with the feature set or trying to extend shelf-life. This should be a key indicator for major updates. – Tom Roberto, Core Technology Solutions

  1. User Testing Of Your New Release Revealed Problems That Need To Be Solved

The update process never stops. In fact, I’d argue the main purpose of any given iteration of your software product is to get to the next iteration. They’re all learning tools helping you to build the next version of your product. As soon as you launch one version, you should be gathering usage data and identifying problems to solve in the next version –- and that cycle continues forever. – Ben Lee, Rootstrap


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

15 Change Management Mistakes You’re Probably Making

July 30th, 2018

It’s often been said that change is the only constant in life. Trite though it may be, this adage rings true, especially in the business world. Markets, technologies and customer preferences are always shifting and evolving, and these factors can have a significant impact on internal operations.

As a leader, it’s your job to not only clearly communicate big changes, but help your team navigate through them. However, this is easier said than done, which is why we asked the experts at Forbes Coaches Council for their advice. Below, they discuss the biggest mistakes leaders make in change management, and how to avoid those errors in your own organization.


  1. Not Developing A Clear Communication Plan For Before, During And After Change

Insufficient communication is the leading problem when it comes to moving employees through change. Organizations that practice transparency and provide information before, during and after the change process will have a more accepting workforce. A communication plan should be developed including different forms of communication taking place at different times. – Karin Naslund, Naslund Consulting Group Inc.

  1. Ignoring The Root Causes Of Employee Resistance

People don’t buy into seemingly bad ideas. Leaders make the mistake of assuming that resistance to change is due to disengaged or difficult employees. In the majority of cases, employees resist change because the information they have tells them this initiative is unwise, ill-founded or unlikely to stick. Leaders need to get curious about the true sources of resistance and take action on those. – Maureen Cunningham, Up Until Now Inc.

  1. Not Asking For Or Incorporating Team Feedback

When instituting change, leaders need to constantly seek feedback on the real-time effects of the changes and be open to adjusting their plan to achieve the desired results. Leaders should be constantly measuring the effects of change and seeking feedback from the affected team members. You then have the data to make adjustments to the plan in order to get the best possible results. – David Galowich, Terra Firma Leadership LLC

  1. Dictating Change, Rather Than Educating People About It

Instituting change from the top down may be your prerogative as a leader but the tendency is to bring this on as a demand and not an education. Companies that bring change on gently find that people adapt and accept it. Education encourages. Harsh decisions will be accepted but often come in more harshly. Training, teaching and peer-to-peer education are the best ways to create positive change. – John M. O’Connor, Career Pro Inc.

  1.  Inconsistent Leadership Involvement

One of the biggest reasons change initiatives fail to materialize is inconsistent executive sponsorship. Change leaders tend to be very vocal and active in the beginning, but with time become inactive. This lack of visibility fuels more resistance to change and runs counter to a successful initiative. Leaders need to stay active and visible throughout the change cycle, not just the beginning. – Ali Merchant, Ali Merchant

  1. Oversimplifying The Change

In my experience, leaders often address change management from an overly simplistic perspective. That perspective is often rooted in their own level of development. Since much of the workforce comes from what’s known as an Expert or Achiever point of view, it’s easy to miss the complexity of change. Such complexity calls for the ability to see the impact of change across multiple systems. – Sharon Spano, Spano & Company, Inc.

  1. Expecting Immediate Acceptance Of Change

People react to a major change in phases, similar to the stages of grief. Understand that buy-in is gradual, and support your team at each phase. Are they in denial? Give them proof with a project timeline. Are you seeing resistance? Comfort them about what they may be losing. Are they exploring with lots of questions? Support them with answers. – Loren Margolis, Training & Leadership Success LLC

  1. Downplaying The Impact Of The Change

Leaders often minimize the change or, more specifically, the impact of the change on engagement and productivity. Leaders have had much more time with the change and have rationalized the impact of the change for themselves. They may also lack the courage to face the change and therefore downplay it to the team. – Michael Brainard, Brainard Strategy

  1. Assuming Employees Know What to Do

Sometimes leaders of change lay out what needs to happen, yet employees don’t know how to move forward. Likely feeling threatened, many employees won’t say anything and become frustrated and fearful for their jobs. Training programs can provide the skills development necessary for success. Coaching programs can help employees identify their emotions and beliefs that might be holding them back. – Barbara OMalley, Exec Advance LLC

  1. Failing To Actively Participate In The Change

There is only so much your change management team can do to prepare, train and reinforce within the organization. The real impact comes from the C-level and executive teams championing the change and leading by example. Leaders can’t expect teams to adopt if they are not actively and publicly participating. – Leanne Wong, Leanne Wong

  1. Neglecting To Involve Those Impacted By The Change

Leaders should decide the “what” of the change and engage others in the “how.” People resist change when it is done to them. When they are a part it, they can see how they’re impacted and how to influence it. One of the first things leaders need to do is identify key stakeholders — both individuals and groups — and identify ways to engage with them early and often. – Edith Onderick-Harvey, NextBridge Consulting, LLC

  1. Not Communicating The Change In A Way That Speaks To Your Team’s Different Personalities

Stabilizers love predictability in their day and aren’t a big fan of change. Organizers want the stats to back up your reasoning. Fixers will step in and help everyone deal with the changes as painlessly as possible. Independents love change and will be pushing you for more. Use “Culture Types” to develop your strategy around roll-out, and you’ll be delighted at its effective implementation. – Dr. Rachel MK Headley, Rose Group, Intl

  1. Trying To Make A Big Change All At Once

Change happens frequently and can be complex. Break change down into manageable steps. Take the most immediate small step that can bring the greatest value. Mastering change comes from being present and seeing opportunities that exist now. Similar to the concept of time to market, be the first to manage the change, no matter how small. – Alan Trivedi, Trivedi Coaching & Consulting Group

  1. Not Helping Others Envision The Possibilities

When changes come, they create uncertainty, doubt and concern among staff. Good leadership requires walking through the upcoming or ongoing change together and casting a vision of the new possibilities that will come about as a result of the change. Help your employees and teammates see the future that you see, and empathize with their potential concerns as you journey together. – Billy Williams, Archegos

  1. Not Outlining And Supporting The Internal Transitional Steps

Change is situational, transition is psychological. Strong change management incorporates both processes. Guide the situational change with clarity of purpose, training and resources, and offer space and time for individuals to digest and process the associated internal transition. Making the steps of transitions known ensures the people will drive the change rather than the change driving them. – Tonyalynne Wildhaber, The Courage Practice


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

Empowering Marketers: When Customer Acquisition Could Go Wrong Without Data Science

July 23rd, 2018

In this fast-paced, fast-food world, advertising is only as good as the science behind it. Customer acquisition is a complex machine requiring attention to all the details and one small slip in the process could cost you millions in revenue. Fancy advertising just doesn’t cut it anymore — you need top-shelf scientific data to get the results you want.

Obtaining quality data is critical especially for startups with limited budgets. Analysts believe (registration required) that companies that use data-driven marketing are six times as likely to be profitable every year and 75% of companies that use it have an advantage over their competition.

Data science allows you to gain valuable insights into who your customers are before spending even one dollar of marketing on them. Demanding consumers are littering the internet with more information than ever. Using this information, you can identify intent and use predictive analysis to hook them in at the right time when they are ready to buy.

Improved Consumer Segmentation — Dig Deeper

It’s not enough to just collect demographic information on your customers. You need to dig deep and know all about who they are, where their interests lie and what kinds of things they buy, why they buy them and when.

According to Google, a person’s online life tells a story, and they call this “I want to moments.” What they do online, where they go and what they click on indicates valuable intent signals. These signals are the gold in the data that you can use to segment and target your audience.

  1. Combine Signals For A Deeper Dive

Capturing one segment of data is not adequate, by combining multiple signals you get a clearer picture and can better pinpoint your marketing strategies. For example, if you gather data on customer search history, this is great information. However, when you combine it with articles they read and videos they watch online, it becomes much more powerful in fleshing out your customer profile. You get to see the whole person come to life in the data.

To finish painting a complete picture of your segment audience, you must collect data from multiple channels. Technology can help with this piece, and programmatic tools will work towards creating a richer, more effective customer profile.

The last thing you want to do as a marketer is to spend money advertising to customers who are not ready to buy your product. Using purchase intent signals and developing predictive models, your team can learn to identify customers who are ready to buy and encourage a purchase.

  1. Fine Tune Your Target Audience With Personalized Marketing

Using a deep dive into customer data will help you identify your target audience to figure out which consumers will be more profitable long-term. Those customers that are more likely to be repeat customers are worth the investment, whereas one-off sales might not be. It’s about lifetime value, not selling to the masses.

One great example of this is a lawn company in Nashville Texas. They started by running a simple Adwords campaign that earned a click-through rate of 1% and a conversion of about 10%. Their CEO Bryan Clayton thought they could do better.

They accessed public records for marketing to find median income levels and property values. They found that East Nashville was populated with a price-sensitive demographic. They then ran a new ad targeted specified to those zip codes which touted “The Cheapest Lawn Mowing in Nashville. Lawn mowing from $20.” This fine-tuning paid off. The new ad generated a 300% click-through rate with a conversion of more than 30%.

The better you know your customer, the easier it is to engage with them and create lasting relationships. It’s not just about selling one item to a lot of customers anymore. These days the marketing game is about customer loyalty, satisfaction and retention.

  1. Email — Still Your Strongest Marketing Tool

Email remains the strongest form of marketing tools with the highest return on investment. Digital strategist Jennie Holmes recommends combining your email campaigns with social media. Instead of following up with the segment that didn’t open your email, target those that clicked on a link and set up a tailored Facebook ad that ties in with the same subject. Social media ads allow you to segment your audience with helpful tools such as customer lists, website traffic data and app activities. Instead of chasing those elusive customers, focus on the ones that are already interested.

Collect The Right Big Data For Your Business

Big data plays a huge role in effective marketing and better decision making. The use of big data makes it possible for you to pinpoint your perfect customer, target a specific set of consumers and fine-tune your marketing techniques and content.

Quality data is a crucial component to marketing success. Without the use of data science and analysis, these efforts would be nearly impossible. All data is not created equal, and you must obtain the most useful and effective data for your marketing strategies through channels that make sense.

The Bottom Line

Without data science, customer acquisition is a guessing game and sales a hit or miss. Integrating technology with quality data allows you to market more successfully with relevant, engaging customer experiences that equals a recipe for success.

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

Preparing Your Business For The Artificial Intelligence Revolution

July 16th, 2018

Today, when asked about artificial intelligence (AI), many people start painting science fiction inspired images of machine-ruled futures and robots completing manual tasks for human beings. To them, AI is only a concept, something that’s going to happen tomorrow.

In reality, artificial intelligence is already part of our lives. We use AI every day. It’s not only on your smartphones, laptops and cars, it’s everywhere.

Is 2018 The Year That The AI Revolution Goes Mainstream?

For the last few years, AI has entered the consciousness of every industry. It has become part of mainstream conversations. Businesses of all shapes and sizes are considering artificial intelligence to solve real business problems.

In the past, only the largest corporations could afford to invest in AI technology, but things are changing fast. In fact, the high-speed growth of AI makes it more likely that startups and younger businesses will be able to embrace the technology earlier than their corporate colleagues.

According to a PricewaterhouseCoopers (PwC) report, the global economy can see a potential contribution of $15.7 trillion from AI by the year 2030. China and North America will receive almost 70% of this potential global GDP growth.

Opportunities For AI Across Industries

Artificial intelligence can be used to solve problems across the board. AI can help businesses increase sales, detect fraud, improve customer experience, automate work processes and provide predictive analysis.

Industries like health care, automotive, financial services and logistics have a lot to gain from AI implementations. Artificial intelligence can help health care service providers with better tools for early diagnostics. The autonomous cars are a direct result of improvements in AI.

Financial services can benefit from AI-based process automation and fraud detection. Logistics companies can use AI for better inventory and delivery management. The retail business can map consumer behavior using AI. Utilities can use smart meters and smart grids to decrease power consumption.

The rise of chatbots and virtual assistants are also a result of artificial intelligence. Amazon’s Alexa, Google’s Home, Apple’s Siri and Microsoft’s Cortana are all using AI-based algorithms to make life better. These technologies will take more prominent roles in dictating future consumer behavior. Most of your future transactions will be completed with the help of an AI-based chatbot or virtual assistant.

Organizations Are Realizing The Potential Of AI

Implementing machine learning and AI is going to have a major impact on your organization’s efficiency. Intelligent systems can automate a great amount of your work and help reduce the risk of human errors. As time goes by, your systems will learn and get smarter. It will result in better outcomes.

Many businesses are starting to recognize the significant benefits and the potential competitive edge they can gain from using AI. There’s a growing interest from every industry about exploring the possibilities.

Organizations are already using artificial intelligence to make practical decisions. For example, Coca-Cola released Cherry Sprite based on their AI product analysis. Furthermore, the soft drink company is planning to create its own virtual assistant to incorporate into its vending machines.

Implementing AI In Your Organization

With so much conversation about AI, there is a real fear of losing out. Remember that it’s only the beginning of the AI revolution. According to Gartner 2018 CIO survey (registration required), only 4% of surveyed companies have invested and deployed an AI-based solution. The rest of the companies are at various planning stages. So if you don’t have an AI-based solution yet, don’t panic. It’s important to start looking at possible tools for your business. Here are a few strategies:

Figure Out Your Business Needs

Take a good look at your business and determine what strategic pain points can be eliminated using AI-based solutions. AI can provide predictive analytics for your business. It can help you automate tasks. Through examination, you can determine the right objectives for your business.

Understand The Risks

Any new technology comes with risks, but the only way to master the technology is through using it and learning from your mistakes. You can take initiatives with smaller scopes and critically evaluate every failure. It will help you understand your risk factors and give you data to make better decisions in the future.

Find Valuable AI Services

To implement AI solutions in the real world, you need AI developers. Due to the fast-growing AI market, developers are a scarce resource. Furthermore, in order to train and deploy the AI applications, developers need access to scalable and affordable computational infrastructure that can support the necessary AI processing. Also, they need raw data and specialists for data labeling, model output validation and more. Building this AI infrastructure is time-consuming and costly. Most companies simply can’t afford to hire 100 data mappers in-house and implement the infrastructure to support them, but there are services available that make AI app development accessible and affordable for businesses. So you don’t have to build the infrastructure and the human capital from scratch. You can use these services to fast-track your AI efforts.

Build The Right Background

To win in any technology war, you need the right people and the right culture. Hiring employees takes time. Building a good culture takes time. You need to invest resources to improve your chances. While it’s expensive to invest in new technology and new talent, it’s worth it. It can transform your business.

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