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The Internet of Things(IoT) has profusely pertinent applications. The effectiveness can be realized through operation and integration of the IoT across applications from domestic use to large scale industrial usage.
In this blog write-up, I would like us to deeply examine dynamics of IoT for SME’s to discover a range of applications and advantages to potentially become a driving force by looking at some of the critical unsolved problems. IoT potential is looked through multiple lenses in this sector, particularly its implications and application across SME landscape.
It is imperative to understand that analytics and IoT are two sides of the same coin. Basically, the information gathered from sensors requires analytics applications. The duo combo will influence to make informed decisions based on the data and behaviors collected.
From this perspective, application and its use have been extended for large industries and organizations. One of the potential benefits that IoT offers is in saving of costs related to process automation and rebound in customer satisfaction.
Similar benefits of IoT can be translated and enabled for SMEs in a relevant scale. SME’s have started embracing IoT and its related technologies. Here we would try to demystify IoT for SMEs such as retailers and companies with more moderate capitals by examining the developing role that it could play in both the immediate and long-term future. Smart devices and sensors are vital for this Machine-to-Machine (M2M) link. By applying machine learning to exploring how IoT could be used to transform businesses, we will envision ways to apply and adopt to SME related challenges
IoT in Retail
Retail across business are a strategic fit for IoT characteristics and intelligent sensors that can measure them. Some areas gaining pace in the industry include Automated Checkouts, Personalized Discounts, Beacons, Smart Shelves, In-store Layout Optimization and Optimizing Supply Chain Management
Sensors acts as the gate way for the above-mentioned areas and are placed at strategic points to capture customers interests, popular and moving brands, kinds of customers etc.
This information will enable customer segmentation and create applications designed for each segment such as promotions or discounts especially during launch offers for new products. In conclusion, IoT for SMEs can enable businesses to design enhanced strategies based on captures information through sensors.
Managing warehouses and production lines
Another potential segment that offers a strong application in IoT for SMEs is warehouse management. Sensors enable to track movement of goods in the warehouse or production lines. They also calculate the count of inventory creating a automated systems to create flags when the merchandise/raw material are running short. Stock replacement/ replenishment requirements can be triggered automatically with alarms.
IoT in supply chain management
Service delivery is another prodigious application of IoT for SMEs. Again, sensors play a critical role in enabling the status of shipment or delivery at every stage. Apart from the above, it is significantly used for calculating improved trajectories for final mile delivery times.
Optimal routes for the delivery to improve the overall customer experience at minimal cost is key application of IoT in this segment.
Predictive and precautionary maintenance
Another application where IoT for SMEs is gaining rapid pace and is highly attractive is predictive and preventive maintenance. Here it enables a system for alerts for early detection and timely replacement of parts or status updates of machines for remote management.
End to end (E2E) operational application of IoT
Intelligent operations begin with integration of data from manufacturing, distribution and sales & marketing divisions. The factual application and advancement of IoT is to integrate all this data in creating new e2e products and services based on preferences of customers in combination to the data collected and validated.
IoT enabled healthcare
Healthcare services and clinics offers personalized service to accompany patients beyond the visits. IoT for SMEs provides solutions for these clinic-based models that result in a competitive advantage. This enables to redesign the dynamics with patients a simple example could be to prompt a trigger for the ophthalmologic patient to replace glasses or improvement tracking. Use of sensors and Big Data also can give the complete vision of an operation and relevant tracking.
Customer based business models
IoT also offers an opportunity for a personalized service for an SME dedicated to plumbing and its predictive maintenance of spare parts to predict pipe installation failure by reviewing its surrounding conditions via application from your cell phone
The above mentioned are some of the many applications and advantages of enabling IoT for SMEs. The principle remains the same exploiting cutting edge technology allows to improve business model through informed decisions based on the data that IoT provides.
Security is a very important part of the above implementation. All technology is vulnerable to attacks. It is critical for the SMEs to consider security as part of the implementation. Below section illustrates some of the guidelines.
Digital Security for SMEs
Security is an essential aspect for SMEs or large organizations. IoT are also highly vulnerable to such attacks. It is important to factor every aspect of your IT architecture with the right security programs. This is key for deployment and commissioning of your sensors and Big Data programs to secure data.
SMEs need to consider aspects of hardware and software associated with IoT implementation model. Emphasis need to be laid on the types of networks, communications and back-up etc. and take inventory of equipment’s, software’s for the type of security that will protect attacks from identified vulnerabilities.
In summary, SME’s are becoming more digital to deliver a connected and seamless experience, IoT will trend among the latest technologies. The emergence of this technologies give rise to newer job roles such as IoT Managers, IoT Business Designers, full stack developers etc. in this sector. The functional and technical areas of these roles span across the expertise of applying sensors, embedded devices, software and other electronics to businesses with front-end and back-end technologies.
The rise of exponential technologies such as IoT and the need to stay upbeat with it, allows scope for the changing landscape of SME’s through new opportunities and roles. IoT will continue to be in the fore front of this changing landscape for SMEs while it is imperative for them directly boost this digital frontier.
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Long considered an impenetrable fortress dominated by a few well-known names, the banking and financial services industry is currently riding a giant wave of entrepreneurial disruption, disinter-mediation, and digital innovation. Everywhere, things are in flux. New, venture-backed arrivals are challenging the old powerhouses. Banks and financial services companies are caught between increasingly strict and costly regulations, and the need to compete through continuous innovation.
How does an entire industry remain relevant, authoritative, and trustworthy while struggling to surmount inflexible legacy systems, outdated business models, and a tired culture? Is there a way for banks and other traditional financial services companies to stay on budget while managing the competitive threat of agile newcomers and startups that do business at lower costs and with better margins? The threat is real. Can established institutions evolve in time to avoid being replaced? What other strategies can protect their extensive infrastructures and win the battle for the customer’s mind, heart, and wallet?
Financial technology, or fintech, is on fire with innovation and investment. The movement is reshaping entrepreneurial businesses and shaking the financial industry, reimagining the methods and tools consumers use to manage, save, and spend money. Agile fintech companies and their technology-intensive offerings do not shy away from big data, analytics, cloud computing, and machine learning, and they insist on a data-driven culture.
Consider a recent Forbes article by Chance Barnett, which quantifies fintech startup investments at $12 billion in 2014, quadrupling the $3 billion level achieved a year earlier. Adding to the wonderment, crowdfunding is likely to surpass venture capital in 2016 as a primary funding source. And people are joining the conversation. Barnett writes, “According to iQ Media, the number of mentions for ‘fintech’ on social media grew four times between 2013 and 2014, and will probably double again in 2015.” All of this activity underscores how technology is rattling the financial status quo and changing the very nature of money.
Yesterday’s Bank: A Rigid Culture, Strapped for Funds
Established banking institutions are strapped. The financial meltdown in 2008 questioned their operations, eroded trust, and invited punitive regulation designed to command, control, and correct the infractions of the past. Regulatory requirements have drained budgets, time, and attention, locking the major firms into constant compliance reporting. To the chagrin of some, these same regulations have also opened the door for new market entrants, technologies, platforms, and modalities—all of which are transforming the industry.
For traditional banking institutions, focus and energy for innovation are simply not there, nor are the necessary IT budgets. Gartner’s Q3 2015 forecast for worldwide IT spending growth (including all devices, data center systems, enterprise software, IT and Telecom services) hints at the challenge banks face: global IT spending is now down to -4.9%, even further from the -1.3% originally forecast, evidence of the spending and investment restraint we see across the financial landscape.
With IT budgets limited, it is hard to imagine banking firms easily reinventing themselves. Yet some are doing just that. Efficient spending is a top strategic priority for banking institutions. Many banks are moving away from a heavy concentration on compliance spending to instead focus on digital transformation, innovation, or collaboration with fintech firms. There is a huge amount of activity on all fronts. To begin, let’s review the competitive landscape of prominent fintech startups.
Data Sciences Intervention
Digital data has snowballed, with the proliferation of the internet, smartphones and other devices. Companies and governments alike recognize the massive potential in using this information – also known as Big Data – to drive real value for customers, and improve efficiency.
Big Data could transform businesses and economies, but the real game changer is data science.
Data science goes beyond traditional statistics to extract actionable insights from information – not just the sort of information you might find in a spreadsheet, but everything from emails and phone calls to text, images, video, social media data streaming, internet searches, GPS locations and computer logs.
“Data sciences enables us to process data better, faster and cheaper than ever
With powerful new techniques, including complex machine-learning algorithms, data science enables us to process data better, faster and cheaper than ever before.
We’re already seeing significant benefits of this – in areas such as national security, business intelligence, law enforcement, financial analysis, health care and disaster preparedness. From location analytics to predictive marketing to cognitive computing, the array of possibilities is overwhelming, sometimes even life-saving. The New York City Fire Department, for example, was one of the earlier success stories of using data science to proactively identify buildings most at risk from fire.
Unleashing the power of Advanced Data Mining using Data Sciences
For banks – in an era when banking is becoming commoditized – the data mining provides a massive opportunity to stand out from the competition. Every banking transaction is a nugget of data, so the industry sits on vast stores of information.
By using data science to collect and analyses Data, banks can improve, or reinvent, nearly every aspect of banking. Data science can enable hyper-targeted marketing, optimized transaction processing, personalized wealth management advice and more – the potential is endless.
A large proportion of the current Data Mining projects in banking revolve around customers – driving sales, boosting retention, improving service, and identifying needs, so the right offers can be served up at the right time.
“Data sciences can help strengthen risk management such as cards fraud detection
Banks can model their clients’ financial performance on multiple data sources and scenarios. Data science can also help strengthen risk management in areas such as cards fraud detection, financial crime compliance, credit scoring, stress-testing and cyber analytics.
The promise of Big Data is even greater than this, however, potentially opening up whole new frontiers in financial services.
Seamless experience for customers
Over 1.7 billion people with mobile phones are currently excluded from the formal financial system. This makes them invisible to credit bureaus, but they are increasingly becoming discoverable through their mobile footprint. Several innovative Fintech firms have already started building predictive models using this type of unconventional data to assess credit risk and provide new types of financing.
While banks have historically been good at running analytics at a product level, such as credit cards, or mortgages, very few have done so holistically, looking across inter-connected customer relationships that could offer a business opportunity – say when an individual customer works for, supplies or purchases from a company that is also a client of the bank. The evolving field of data science facilitates this seamless view.
Blockchain as the new database
Much more is yet to come. Blockchain, the underlying disruptive technology behind cryptocurrency Bitcoin, could spell huge change for financial services in the future. Saving information as ‘hash’, rather than in its original format, the blockchain ensures each data element is unique, time-stamped and tamper-resistant.
The semi-public nature of some types of blockchain paves the way for an enhanced level of security and privacy for sensitive data – a new kind of database where the information ‘header’ is public but the data inside is ‘private’.
As such, the blockchain has several potential applications in financial markets – think of trade finance, stock exchanges, central securities depositories, trade repositories or settlements systems.
Data analytics using blockchain, distributed ledger transactions and smart contracts will become critical in future, creating new challenges and opportunities in the world of data science.
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One might be tempted to think we are living in a startup bubble, with investors being largely optimistic about startups and investing millions of dollars in them, with many startups crossing the billion dollar valuation on a regular basis. But managing a startup is tough, with almost unreal targets set in between funding rounds. The founders need to, at all times, be focused on the direction in which they need to head, and be sure of the selective performance indicators that they need to keep watch of. Creating data has become easy at current times. Though acquiring data from multiple sources has its potential benefits, but for a company at its seminal stage, dealing with multiple KPIs is a huge risk. Startups can easily get side-tracked by following the wrong KPI. In an ideal scenario, startups should keep only one performance indicator and keep scaling up in that direction before achieving a milestone and involving others in the development plan. With a large number of startups around, existence of a red ocean, and ample amount of data giving number of insights and scope for strategies, implementing analytics can be a sure shot way to keep startups focused on the optimal way to scale up, and in extension create organic buzz and visibility to scale further. Analytics has the capacity to point out which should be the root nerve of a startup and how to scale further in that direction. The seminal stages of creating a high visibility in the market and expansion of a startup involves a repeated cycle of – Building and improving of the Core Competency, and measuring the effect in KPIs and increase in adoption by customers. Let’s see where and how analytics can be optimally implemented for these scenarios.
Building Core Competency
The starting point for understanding core competences is understanding that businesses need to have something that customers uniquely value if they’re to make good profits. “Me too” businesses (with nothing unique to distinguish them from their competition) are doomed to compete on price: the only thing they can do to make themselves the customer’s top choice is drop price. And as other “me too” businesses do the same, profit margins become thinner and thinner.
This is why there’s such an emphasis on building and selling USPs (Unique Selling Points) in business. If you’re able to offer something uniquely good, customers will want to choose your products and will be willing to pay more for them. Here are three ways to turn analytics insights into actions that make your company more competitive:
Gain control along with visibility of patterns
People often use analytics to understand what’s already happened, but don’t look beyond “what”, to ask “why.” By understanding why certain patterns emerge in your data, you gain greater visibility and control over what’s happening right now.
For example, when you understand why certain factors affect your margins, your sales team is better able to address underperforming products and customers, identify potential revenue opportunities and design more optimal coverage models for your reps.
Put analytics in the right functional areas to drive change
To get results, you need a way to deliver analytical information to sales reps at the product and account level. This empowers reps to negotiate from an informed position and use data to have strategic conversations with customers.
Also, when reps have good access to customer analytics, they’re better able to invest coverage resources in high-quality leads. It helps them to identify opportunities with large value and position sales offers in the context of a dynamic market. For example, if there’s a lot of variability in a commodity and price wars break out, you want to quickly reposition your offer in relation to that dynamic market.
Build an ecosystem
To get the best results from your analytics, you need the ability to monitor what’s happening and use that data to adapt. As you build this process into your company’s DNA, constantly evaluate the criteria you’re using to ensure they stay relevant: Are you looking at the right variables and assessing the marketplace effectively? By maintaining the quality of this information, you’re developing a competitive advantage through pricing and sales sophistication.
Measuring Business Traction
Analytics helps your business determine what is working well, and what needs to be improved. We can always go off of a hunch, but the real power comes when we know the hard data behind our marketing or business management efforts, and can make informed decisions that improve our business over and over. Seasoned entrepreneurs know just how important analytics are in growing your business. Without a serious analytics strategy, you are simply relying on hope and luck to grow your company.
In a startup you are constantly under pressure and have way too many distractions. Having a set of metrics that you watch & that you feel are the key drivers of your success helps keep clarity. And the more public you can make your goals for these key metrics the better. Make them widely available inside the company and share your most important goals with your board. Transparency of goals drives performance because it creates both a commitment and a sense of urgency.
If you don’t have a stability goal stated for the company and if you don’t regularly measure how you’re doing against this goal you won’t have your resources focused on the right priorities in the company.
Most companies have some measurements, but I would argue that people often measure the wrong stuff, measure with the wrong precision. The best way is to start by asking yourself at management team level: what are our company objectives and how do we best measure them? Because it can be hard to define or agree company objectives at an early stage I believe most people avoid them.
At the highest level you’ll obviously want to track how many customers you’re adding every month (and for some businesses that have hit scale this is measured on a daily basis). If you can break this down by channel that you’ve acquired them from this is obviously better.
How many additions came through organic SEO? How many through affiliate deals? How many through SEM? Do you have a customer referral program? If so, make sure you can track which leads come from this. Measuring viral adoption is obviously important.
Usually you have a catch-all bucket for “direct” or similar that often came through PR or word-of-mouth.
If you have multiple versions of your product, how many are web vs. mobile? How do the mobile customers break down by device type?
The next step after measuring the customers you’re adding is to add the “cost to acquire” by channel. This is important because it will later tell you whether you have a scalable business or not. In the early phases if you can’t acquire customers cost effectively enough you’ll need to diagnose why and how to fix it.
The Final Question of Scalability
The repeated cycles of Building and re-engineering and Core Competency and Measuring the Market environment effects takes the startups further and further into the final stages of having a scalable model. Like I mentioned at the beginning, there could be n number of directions a startup can head towards, as many as there are number of significant KPIs that need to be improved. But finding the right nerve and chasing the wrong performance indicator is the difference between ending up with a scalable business model and ending up with a marginally incremental model. In this current age of every changing topography of the market with disruptive ideas entering and washing off many hopeful businesses, only having an optimal analytics solution to track their locus can make sure startups sustain and succeed.