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

August 13th, 2018 by blogadmin No comments »

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, Doctor.com

  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, Pipedrive.com

  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.

https://www.forbes.com/sites/forbestechcouncil/2018/06/06/14-ways-to-protect-your-key-data-without-buying-more-software/#54dc7f3f42a4

11 Signs Your Software Is Due For A Major Update

August 6th, 2018 by blogadmin No comments »

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.

https://www.forbes.com/sites/forbestechcouncil/2018/06/15/11-signs-your-software-is-due-for-a-major-update/#53f764023260

15 Change Management Mistakes You’re Probably Making

July 30th, 2018 by blogadmin No comments »

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.

https://www.forbes.com/sites/forbescoachescouncil/2018/05/23/15-change-management-mistakes-youre-probably-making/#3e969c392482

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

July 23rd, 2018 by blogadmin No comments »

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.

https://www.forbes.com/sites/forbestechcouncil/2018/07/23/empowering-marketers-when-customer-acquisition-could-go-wrong-without-data-science/#3df2e678626e.

Preparing Your Business For The Artificial Intelligence Revolution

July 16th, 2018 by blogadmin No comments »

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.

https://www.forbes.com/sites/forbestechcouncil/2018/07/12/preparing-your-business-for-the-artificial-intelligence-revolution/#7ffc7d497ac8

 

Do You Fear Artificial Intelligence Will Take Your Job?

July 10th, 2018 by blogadmin No comments »

Artificial intelligence (AI) has been around longer than most people realize. The intent behind much of AI is to free us from mundane repetitive tasks, giving us more time to grow our intellects and businesses, with more interesting, evolving actions. We want what we want when we want it. AI offers us that access with speed and accuracy when we need it.

In London, self-driving robots deliver food. In Pasadena, California, a robot named Flippy can cook it. Last fall, an autonomous train made its way across the Australian outback for the first time, and Zhuzhou, China, began testing a trackless and driverless train that navigates city streets by means of lines painted on the road. From writing articles for The Washington Post to creating music, artificial intelligence is everywhere. And its adoption is rapidly becoming necessary for businesses to stay competitive.

How does this affect human employees? As co-founder of a company that utilizes artificial intelligence to provide customer support solutions, I believe that low-skilled jobs are most likely to be affected and most chances of being automated. White collar jobs are also at risk though with AI taking a bigger role in the financial industry.

But despite all this, the future for human employees may be much brighter than many recent predictions. While AI destroys jobs, it also creates them. And according to a report from the research firm Gartner, artificial intelligence is currently creating more jobs than it destroys, with a net increase of over two million jobs by 2025. This includes not only the obvious jobs such as software engineers but also low-level jobs such as training AI to recognize objects or human activity and many others.

AI may destroy jobs and it may create them, but it’s not always about man versus machine. AI can be at its best when it helps humans to perform jobs. For example, last year, Walmart announced it was beginning tests of shelf scanning robots at 50 locations. These robots are not intended to replace human workers but to make them more efficient. The robots free employees from the tedious task of walking the aisles looking for out of stock products and allow them to focus their time on filling the shelves, replacing items left in the wrong place and fixing problems that the robots notify them of. The goal here is to reduce the number of times a customer looks for an item only to discover an empty shelf.

In the pharmaceutical industry, artificial intelligence can take on tasks that human minds simply can’t do. According to a study from Tufts University, it can take over a decade and cost over $2.5 billion to develop a drug from start to approval and market. However, most drugs don’t make it to market, some failing early, but others failing close to the end when years and millions or billions of dollars have already been spent. AI can leverage the vast amounts of data regarding medicine and health, thus potentially lowering the rate of failed trials. It can also help find appropriate patients to participate in clinical trials, model the behaviour of molecules to help predict how they will behave in the human body, and find genetic biomarkers that allow medicine to be tailored to individuals.

Artificial Intelligence Needs Humans

In the above examples, artificial intelligence plays a part in preventing human errors. However, AI also still needs human oversight to prevent its own errors.

In July of last year, a bot designed to create phone cases based on popular image searches went terribly wrong and began creating cases with disturbing medical imagery and inappropriate images which were listed for sale on Amazon by the third party seller. In a far less amusing example of AI gone wrong, it took less than a day for Twitter to teach Microsoft’s AI account “Tay Tweets” to spout racism, sexism and love for Hitler. To prevent such malformed sentences, AI models need more training data and a proactive human oversight.

The stakes grow far more serious when AI operates heavy machinery or is involved in healthcare. Autonomous vehicles have been lauded for their potential to reduce collisions, 94 percent of which are caused by human error, according to the NHTSA. After all, autonomous vehicles won’t drive while drunk, tired or distracted. However, autonomous vehicles have already failed to prevent two deaths despite the presence of safety drivers. The safety driver of a 2016 Tesla S may have relied too heavily on the autopilot. Data showed that he ignored seven warnings to return his hands to the wheel before the vehicle failed confused the white of a tractor-trailer for open sky and drove right into it. Though we all believe, the autonomous vehicles would become the norm in the next decade, safety regulations and substantial human oversight are very much needed and will be needed for the foreseeable future.

From exploring places humans can’t go to finding meaning from sources of data too large for humans to analyze, to helping doctors make diagnoses to helping prevent accidents, the potential for artificial intelligence to benefit humans appears limitless. There is valid concern that even as AI saves lives and helps businesses thrive, it will destroy livelihoods. Without a doubt, AI is taking over jobs once done by humans. However, it also creates jobs, and AI needs people to train it and watch over it. At its best, AI works with people instead of in place of them — removing the tedious parts of jobs so employees can focus on better things, doing tasks that humans were unable to, and helping employees better do their jobs.

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

https://www.forbes.com/sites/theyec/2018/07/06/do-you-fear-artificial-intelligence-will-take-your-job/#65830b3c11aa

A Crossroads: Artificial Intelligence And Advertising

July 2nd, 2018 by blogadmin No comments »

According to Elon Musk, artificial intelligence (AI) is “our biggest existential threat.” Whether that is fact-based or hyperbole is determined by the reader’s perception of Musk as either the real-life Iron Man or a classic hype marketer. Whether Elon is trying to wake humanity before it’s too late or simply sell tickets on a Mars-bound Tesla is beside the point. The focus here should be that Elon Musk has discovered and successfully exploited the intersection of AI and advertising and so can you.

We have reached an inflection point in the advancements in, and democratization of, artificial intelligence. The technology allows businesses to harness its value daily to realize new business development opportunities and capitalize on tangible results — chief among them is a more intelligent way to advertise products and services, verify campaign efficacy and measure return on investment. On top of this, and perhaps in direct contrast with Musk’s comments, legacy radio and television are poised to be the prime benefactors of this AI-driven advertising revolution.

For many years, digital advertising has been the safest and arguably most sound bet in advertising. Cookie tracking, intellectual property (IP) targeting and other technologies allow brands and agencies to gather user and audience data in order to target their advertising campaign placement, track overall efficacy and measure return on investment (ROI) down to the click. This level of ROI transparency is tough to argue with and ultimately led to digital ad spend reaching $209 billion worldwide, which accounted for 41% of the market in 2017. Digital finally eclipsed traditional TV spending, which brought in $178 billion, or 35% of the market. With no definitive way to target, track and measure advertising efficacy, radio and television have been struggling to compete with digital until now.

While digital advertising currently makes up the lion’s share of brand and agency advertising spending, traditional radio and television companies are fighting back in a big way thanks to artificial intelligence. Developments in natural language processing, logo recognition, object detection and other AI technologies have enabled radio and television broadcasters to bring structure to a medium that has been heretofore impossible. With every word, logo, object and face indexed in near real-time, radio and television content becomes just as searchable, trackable and actionable as digital content. This is critical because without true structure — a temporal record of exactly what aired — agencies and brands had been struggling to successfully target, engage and unlock the value hidden within radio and TV. Now, with AI, legacy challenges have fast blossomed into new opportunities and there is plenty of nectar to go around.

Just take it from top media agency Carat’s chief compliance officer (CCO) Shannon Pruitt, who, in speaking to CNBC regarding product placement within traditional distribution windows, said, “Advances in audience targeting, the understanding of the role of product integration, as well as the focus on measurement capabilities to prove ROI, while still not comprehensive, has elevated the acceptance and pursuit of the opportunity to be part of the story.”

In other words, the convergence of AI technology used for audience targeting and ROI evaluation with the shifting desires of TV and radio audiences away from traditional interruptive-based 30- and 60-second commercials, has agencies and advertisers alike rushing back to TV and radio to cash in on new in-content branding opportunities. According to PQ Media, the “value of U.S. product placements will reach $11.44 billion in 2019,” surging up from $6.01 billion in 2014. This type of growth for traditional mediums who had been left with mere scraps in recent years is one very concrete example of how AI, when leveraged appropriately, can revolutionize an industry overnight.

So whether you are looking to streamline operational efficiency, innovate products or unlock additional revenue streams, you might consider taking a page out of Elon’s book and look towards artificial intelligence.

 

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

https://www.forbes.com/sites/forbesbusinessdevelopmentcouncil/2018/06/19/a-crossroads-artificial-intelligence-and-advertising/#293e640360b3

How Tech Enterprises Handle Big Data on Open Source and Ensure User Privacy

June 26th, 2018 by blogadmin No comments »

The term “big data” gets thrown around a lot, especially taking into account its importance for driving AI technology. Finding ways to build scalable systems that provide valuable insights into what you’re doing well and what you could be doing better is imperative to maintain a competitive edge. And, as big data, artificial intelligence and machine learning become more advanced and interconnected each year, these scalable systems become more and more valuable.

When PicsArt was founded in 2011, the online landscape and the world of data collection, management and analysis were much less sophisticated. Since then, many startups have risen while others have faltered, and those that have found success were largely companies that were able to adapt to an increasingly data-driven marketplace. Today, our users generate a staggering 10 terabytes of data every single day. On a global scale, PicsArt has a medium- to large-size big data cluster with most of the large-size cluster functionalities enabled.

It was evident that we were stepping into the big data arena when our data met all four characteristics of big data: volume, velocity, variability and complexity. Once the volume of data we were dealing with was too large to fit into a relational or other standard database, the die was cast and we jumped into the big data scene with optimism and gusto. Besides that, because AI and machine learning became a mainstream technology, we were able to fully use it to benefit our users.

Adapting To A Big Data Mindset

When most people think about big data, they often imagine that the technical side would be the most difficult, but we found out through trial and error that approaching problems from a technical side first isn’t always ideal. Big data offers nearly endless possibilities, but if you don’t have a clear understanding of specific use cases and goals, you can unnecessarily prolong the development process. Since our system was constructed without a clearly delineated list of use cases, our data architects had to design it to handle as many future use cases as possible. The end result was a working system, with extensive support and capabilities, but the rollout time was longer than it could have been had we defined things better from the start.

Getting used to the sheer scope of data was a learning curve as well, especially since there was a lack of a big data community at the time. Initially, we placed responsibility for cleaning data on a single centralized team, which we quickly discovered would never work due to the constant barrage of thousands of events happening across multiple apps. Getting the data clean, we discovered, requires simultaneous efforts from the tech and business teams — it only works if everyone is on the same page. Big data is considered the new oil nowadays, but it’s also a huge challenge in terms of how to prepare it, process it, store it and most importantly, turn it into applicable knowledge. To make that happen, it’s important to define the most common use cases within the product and align technical and business team efforts from the beginning. Overall, maintaining flexibility, learning from mistakes and adapting was essential to getting past the first step to becoming a big data company.

Finding The Right Tools For The Job

As the value of big data became more evident, conferences started popping up, giving innovators and companies a way to gather and share strategies. Open source solutions for data analysis and collections became more common and more robust, and it got easier to find the right technology. The lesson that start-ups can take away from all this is to take advantage of the big data community that exists now and do so with direct aims in mind.

In a wide range of tools, it’s really important to find those that fit your business needs. That can be done only empirically depending on the size of your company. It is important to discover tools for data processing, data analysis, crash monitoring and infrastructure monitoring.

Using Data To Fuel Innovation

Each piece of data my company collects falls into one of three categories: user device info, user behaviour and uploaded images — complete with editing logs and intermediate steps. The metadata we collect is used to directly improve the user experience by responding to the way people use our app and then creating the tools they want.

Privacy is definitely an important topic for every tech enterprise that deals with a large amount of data. As an organization operates globally with data on citizens in European Union countries, they must comply with strict new rules around protecting customer data: The General Data Protection Regulation sets a new standard for consumer rights regarding their data. All of our users have the opportunity to adjust their preferable privacy settings and make sure they are comfortable with the data they are sharing with us.  

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

https://www.forbes.com/sites/forbestechcouncil/2018/06/22/how-tech-enterprises-handle-big-data-on-open-source-and-ensure-user-privacy/#172964e3309d

Organizations Striving to Close the Data Science Skills Gap

June 18th, 2018 by blogadmin No comments »

Big data is undoubtedly one of the hottest trends of our age, and the promise of enormous amounts of data to fundamentally transform how our organizations operate is considerable.  For many however, the promise remains just that, with numerous barriers holding them back, whether it’s a lack of board level buy-in or poor quality data.

Arguably the most substantial drag on our efforts however has been a lack of skills.  It’s a situation that is likely to see companies aim to triple the size of their data science teams in the next few years.  That’s the finding of a recent paper from ESADE researchers.

The researchers examined over 100 Spanish companies from across a range of sectors, most of which had over €200 million in turnover.  The results revealed the long way we still have to go before data is at the heart of organizational behaviour.

Slow progress

Despite big data being technologically feasible for several years, over half of the organizations revealed that they are yet to have a culture of data-based decision making, whilst 40% admitted that they don’t have a specific leadership role for data.

This reticence is important, as the study found that companies with a more analytical culture performed better than those without.  This was reflected in both their financial performance and the perception of staff at the companies.  Indeed, some 78% of companies who were regarded as very analytical thought that this culture had a significant impact upon their performance.

The study found that data professionals tended to fall into one of two categories:

  1. Data scientists, who tend to perform advanced analyses.
  2. Data managers, who provide the business vision to connect these analyses to the strategy of the business.

The typical data team would have between 5 and 20 members, but pretty much every organization reported finding it difficult to find the talent they needed.  Despite these recruitment challenges, the majority of organizations wanted to considerably increase the size of their data teams in the next three years, with three times as many data scientists and 2.5 times te number of data managers.

Train or recruit?

The desire for data science skills is clear, but this study suggests that most companies want to hire in external talent, or in other words the finished article.  This strategy would be fine except by all accounts, that talent isn’t currently existing in the marketplace, so there appears to be an inherent hope that external bodies will train people for them.

A post was written previously about a similar issue when it comes to artificial intelligence skills, and data science and AI are so intertwined that the same surely applies.

Rather than attempting to hire in the finished article in an increasingly barren marketplace, companies are surely better off investing in data-science training and therefore upgrading their existing talent pool.  This approach has numerous advantages, not least of which is raising data skills across the board at a time when a growing number of organizations are attempting to democratize data science capabilities across the workforce rather than concentrate it within a data science function.

Organizations can achieve quick initial results by identifying employees with existing programming, analytical and quantitative skills and augmenting them with both the latest data-science skills and access to powerful tools, such as Python and Hadoop.

Spreading the availability of data education across the business, into marketing, finance, engineering and various other functions provides data literacy to people from various backgrounds.  This in turn will help to spread the data-driven culture that data advocates so crave.

A good example of this in practice is the Data University that Airbnb have created to provide anyone who wants to learn about data an opportunity to do so.  Already the company has trained over 500 (or 1/8th of the workforce) employees, with dividends already being reaped in the shift towards data-based decision making.

There has never been a better time to invest in the skills and talents of your workforce, with data promising to transform functions and processes throughout organizations that are already experimenting with a range of data science and machine learning initiatives.  Expertise is the principle barrier holding these back, so now really is the time to invest in the training that will bridge that gap.

 

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

https://www.forbes.com/sites/adigaskell/2018/06/18/organizations-striving-to-close-the-data-science-skills-gap/#1aeab8291d50

 

SAP Mounts Formalized CRM Drive

June 11th, 2018 by blogadmin No comments »

SAP has formalized its approach to Customer Relationship Management (CRM) by consolidating upon recent acquisitions and integrating these functions into its existing stack of database and data analytics technologies.

In specific terms, SAP has now brought together acquisitions including Hybris (acquired in 2013 – CRM and commerce software), Gigya (acquired in 2017 – customer identity management technology used to manage customer profiles, preferences, opt-ins and consent settings) and CallidusCloud (acquired in 2018 – technology that links salespeople with information related to pricing, incentives & commission all linked to a firm’s Enterprise Resource Planning (ERP) systems) — the combined sum of these parts will now be known as SAP C/4HANA.

As Forbes writer Bob Evans notes here, the overall technology proposition here is a direct play (Evans calls it a ‘head on assault’) at Salesforce, but with what SAP claims to be a more holistic connection to an enterprise’s deeper software stack and ERP systems, SAP’s bread and butter. This makes it CRM engineered more directly into a business supply chain. If you believe the marketing, this is what all the vendors like to call a 360-degree view of the customer.

With SAP’s existing business suite being labelled SAP S/4HANA, the firm has obviously adopted the same naming convention replacing the S-for suite with C-for CRM. The company’s drive to build a more established CRM offering will see it go head to head against not just Salesforce, but a selection of established players in this space including Oracle, Dynamics 365, Verint, Pegasystems and others.

Customer Experience division (the new SAP grouping that includes Hybris, Gigya, Callidus and other elements) president at SAP is Alex Atzberger. Suggesting that there have been four eras of CRM through the ages, Atzberger details them as:

  1. Basic customer sales-based lead optimization systems.
  2. So-called ‘point’ solutions designed to address one specific CRM issue.
  3. Cloud-based systems.
  4. More intelligent holistic connected CRM systems that connect the customer experience to the actual supply chain that an enterprise operates on a day-to-day basis.

Lamenting what SAP CEO Bill McDermott has called the “sales-only focus of legacy CRM solutions”, SAP thinks it can offer a new notion of CRM that exists in the 4.0 age. This is CRM that is more intrinsically engineered into (and integrated with) a customer’s wider software stack of applications and database management systems – and indeed the enterprise demand and supply chain.

“SAP was the last to accept the status quo of CRM and is now the first to change it,” said McDermott. “The legacy CRM systems are all about sales; SAP C/4HANA is all about the consumer. We recognize that every part of a business needs to be focused on a single view of the consumer. When you connect all SAP applications together in an intelligent cloud suite, the demand chain directly fuels the behaviours of the supply chain.”

In line with its new CRM offering SAP has also announced the SAP HANA Data Management Suite. This is software designed to combat what has been called ‘data sprawl’ resulting from firms who operate with highly distributed data that exists in lots of different locations, on different devices, on different platforms, in different states (structured, semi-structured and unstructured) and in different business workflows and business processes.

The SAP C/4HANA suite will offer full integration with SAP’s business applications portfolio, led by the SAP S/4HANA ERP suite.

Crowd-service: more help, from ‘any’ employee

There’s one other add on here for customer service. SAP has also acquired Switzerland-based Coresystems AG to improve field-service customer experience, especially in the manufacturing, energy, high-tech and telecommunications industries. This software service provides scheduling for customer-service requests and uses artificial intelligence-powered crowd-service technology. SAP insists that this broadens the ‘service technician pool’ (those people able to fix any particular problem that occurs in a company during its working day) to include company employees, freelancers and industry partners. The ‘crowd service’ concept means that enterprises can assign the best-qualified technician (or person able to help) to each service call by taking into account expertise, location and availability.

“All systems rely on data, yet the challenge facing companies today is distributed data — data that is not just in transactional systems but scattered across products, machines and people. It is about data that must be ingested, prepared and made enterprise relevant. SAP HANA Data Management Suite enables enterprises to turn massive amounts of data — both structured and unstructured — into valuable, usable knowledge, no matter where it resides,” notes SAP, in a product launch statement.

The SAP C/4HANA portfolio includes SAP Marketing Cloud, SAP Commerce Cloud, SAP Service Cloud, and SAP Customer Data Cloud (including the acquired Gigya solutions) and SAP Sales Cloud (including the acquired CallidusCloud solutions). Additionally, SAP Sales Cloud unites the SAP Hybris Revenue Cloud solution and SAP Hybris Cloud for Customer (comprised of SAP Hybris Sales Cloud and SAP Hybris Service Cloud solutions).

The SAP Hybris name (along with other acquired firms noted in this story) will now be retired to consolidate under the SAP Customer Experience business unit.

The real challenge here is…

Whether the next generation of CRM actually results from one vendor firing pot-shots or thinly-veiled swipes at one another or not, the big question here will come down to implementation, integration and interconnectedness of the systems being built.

As already suggested here, success in the 360-degree connected CRM world is a question of real end-to-end real-time synchronization between the demand chain and supply chain. That means using ERP and CRM — and a list of other favourite tech industry acronyms including Field Service Management (FSM), Human Capital Management (HCM), IT Service Management (ITSM) and more – and being able to access the data that resides in the clouds serving those functions.

Unless we the humans can get access to the right data, in the right cloud services, serving the right business processes, in the right configuration patterns… then we won’t be able to physically get our developers to code the right functional ‘scripts’ into the codebases that run the so-called ‘smart’ (CRM or otherwise) applications of the future.

There’s a gap in between pure theory and applied empirical success here and SAP will obviously now be working hard to make sure it has customer reference points to convince us that its vision holds water. Claims that CRM is dead and that we can now shout long live 360-degree ERP CRM require deeper analysis and the journey is just starting. This revolution will be televised.

 

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

https://www.forbes.com/sites/adrianbridgwater/2018/06/05/sap-mounts-formalized-crm-drive/#47b49b974823