Tag: big data
If you are planning to select Business Intelligence (BI) tool for your Big Data solutions, it is important to evaluate which one is the best suited and not best rated for your company. Selecting a right visualization tool that can help you get the most out of Big Data and has well-defined functions, is an important criterion of the process. So you should ask the following questions before selecting the best tool for your company.
1. What are you visualizing?
It is important to first understand why you are looking for the tool in the first place. If you are planning to visualize the internal data such as marketing, finance, etc. you should look for a tool that is in alignment with your management system. For example, if you are using SAP ECC/Net Weaver system for handling internal data, an SAP-based BI will work better for easy implementation and cost reduction on training. Similarly, if you are going to use the tool for a client, it is better to use something that is compatible with what your client is using.
2. How is the tool’s interface?
It is imperative that the tool has an easy to use Graphical User Interface (GUI). Tools are meant to save time and make the task easy. A well-designed tool that offers access to various options can be put in the pipeline with ease. Check if it has nice graphics capabilities in case you need to visualize decision trees and so on.
3. Does it have the essential support for visual discovery?
Tools should provide the most basic support for visual discovery and query processing. This might include something as simple as comma-separated values file, text, Excel, and XML support. Apart from these basic things, you might need to check what programming language it supports. Your decision will rest on what your internal team is expert at handling. Your team can get to support for various well-known programming languages such as C++, Python, Java, and Perl.
The other thing to check is whether or not the visualization tool you are planning to use is compatible with the operating system you use. In case of cloud implementation, ask the cloud provider for an OS that is compatible with your visualization tool. If you are catering to a client, ensure that the OS you select is compatible with their systems too.
4. Is the price right?
It is no surprise that price plays an important role in finalizing a lot of things in any company. BI projects cost a lot and the cost will largely depend on a number of criteria such as the level of in-house expertise and the ultimate goal to be achieved. Visualization tools should not be judged on the basis of their price alone but compared with how big is the need and what is being provided.
A good way to make a decision is to try a free trial version of the software to check whether it works for you or not. The tool provider should offer good technical support along with the documentation that covers all aspects of the tool.
5. How flexible is the tool?
Big Data is evolving at a phenomenal rate and so is the technology around it. Make sure that the visualization tool that you are seeking is flexible enough to adapt to these changes. Ask the provider how easy it is to upgrade the tool so that you do not hit a roadblock and require a complete overhaul in the near future.
Understanding these points will help you start zeroing on a list of visualization tools but seeking the support of an experienced tool provider will help you finalize it. Look for someone like us who have an expertise in understanding the requirements of the client and providing a complete solution.
When companies target to stabilize their growth engine to reach the next level in business and increase the revenue, most of them make decisions with the data about prevailing market conditions from third-party vendors, which might turn their fortunes upside down. The problem is persistent in many industries and the companies have been fighting hard to win the race with the right data set. There are only a few companies that use Big Data sourced from their own operations and make decisions based on that data for laying out the future course of the company.
It has been estimated that companies that are data-driven are five percent more productive and six percent more profitable than other companies. So the question remains, how to use Big Data in making business decisions, especially for CIO’s, because their role in implementing IT-based solutions in a business is invaluable.
It has been time and again noted by the “Big Four” that CIOs who have taken an active lead to use Big Data exemplify a unique liberty to transform their businesses, and to make their roles more strategic and more influential than ever before. There is a debate wherein we have questions that if a CIO or anyone in an analytics specific role like Chief Analytics Officer or Chief Data Officer should lead a big data initiative or not, and the debate is getting more stale day to day because the key to lead Big Data initiative is to being innovative with the moving parts of the business. Most CIOs have settled in with an operational mindset and Big Data is giving them the answers to everything they are looking for. If we are having a CAO or CDO to chip in and lead big data initiatives, it would become another stale part of the company as they don’t have the liberty to fine tune the moving parts of the company like a CIO does.
But there are further problems to address before we get onto ‘Why, When, How,’ Of using Big Data. There was a time in the past when we couldn’t aggregate Big Data, It was too expensive. But times have changed now, storage has become cheaper and cloud has enabled us to collect Big Data. What can we do with Big Data? There are companies that already have Big Data but they stand disappointed as it’s not creating value. The first essential step is to find the correlation in the data through Machine Learning, then only we can begin with predictive analytics.
Internet of Things(IoT) has been fueling the growth in the industrial sector which is on the hunt for more data. For the industrial sector, it is a completely different ball game as they can’t connect everything to the cloud due to security reasons, a lot of correlation would be eventually done on the cloud but some of that data is going to be processed on the edge. Only a Data Scientist with a very good domain expertise of the industry can help optimise Big Data for Machine Learning. Else, we won’t get an optimum result.
This architecture where CIOs are able to get maximum value out of Big Data is very much essential and can only be achieved if we integrate every moving part of the business with data. Data which can be stored, processed and correlated to solve problems and find solutions.
When a business is trying to respond to the customer that expects much more rapid change, IT must respond to that change. We can see a rapid change where companies are investing more on Cloud Computing, Big Data & Analytics rather that legacy desktops and server applications. CIOs are the driving force behind this shift and their effort to enforce such changes should always be appreciated. A CIO in the present day needs to be a full-blown business leader who understands both the impact of advanced new technologies on business metrics and how to make these new technologies effectively.
Another important aspect is that CIOs should keep asking questions like “What if we try to do it this way? How could this change make an impact for the business?”. This type of ability to imagine the unknown is what CIOs need to unlock and build models that predict and optimize business outcomes.
The past decade has been a game-changer for the way businesses operate in the realm of retail. The advent of e-commerce and its subsequent boom has compelled brick and mortar outlets to undertake a paradigm shift from a profits-first to a consumer-centric approach. Failure in conforming to new consumer demands fueled the retail apocalypse that toppled the brick and mortar landscape. Thus, we see retail giants like Bon-Ton Stores Inc., Sears and Macy’s filing for bankruptcy and liquidate their holdings.
Implementation of targeted mobile promotions, loyalty benefits, e-payment gateways is just some of the strategies adopted by retail outlets to maintain a competitive advantage in the face of fierce technological overhauls. With disruptive innovation gaining a strong footing now more than ever, the need to constantly reinvent and augment is more pressing than before. Here are five key disruptive technology trends that you need to sync your business model with, to offer consumers retail experience par excellence:
Related Reading: 5 Ways to Enrich Customer Experience at Your Retail Store
1. The advent of Artificial Intelligence
Robots and AI bots capable of not just learning but also executing real smartness are the new focus in tech innovations. Retail giants are already experimenting with ways to implement these AI bots in their business operations. A strong case in point would be Amazon’s no-checkout cashier-less convenience stores, Amazon Go being tested across different states in North America.
Then there are self-driving grocery stores and automated trucks making home deliveries that are still undergoing trials. Of course, that is not to say that AI dominating retail operations will become the new normal tomorrow, but it is in the offing. Businesses that are in the retail sector for the long haul will stand to gain from their preparedness to embrace this change.
2. The Internet of Things
The ability of devices to interact with humans, understand commands and execute them is passé. The Internet of Things (IoT) puts the limelight on the ability of machines to interact with one another. The slow but consistent development of IoT is shaping up a new ecosystem where our gadgets will be able to operate without human intervention. Besides, the global market size for IoT in retail is expected to grow around 94.44 billion by 2025.
The emergence of IoT will inevitably alter the dynamics of the way consumers interact with retail business and the way businesses interact with distribution networks and supply chain partners. More importantly, it will usher in a connected customer model by relying on smart-store applications like smart shelves, beacons, and customer service robots. Making room for these swift connections powered by the internet will help you build a business model that is future ready.
3. Striking the Online-Offline Balance
It is the age of digital customers where the lines between online and offline existences are forever blurring. Brick and mortar businesses need an online extension to sustain themselves. Now, the spotlight is on understanding the dynamics of virtual and augmented reality and creating a marketing strategy that caters to the customers’ dual persona – considering their social media image and real identity – to encourage continued interactions and conversions.
The result – a complete overhaul of the shopping experience by bringing in a consistent omnichannel approach built around a convenient digital backend. For instance, Oasis, the UK-based fashion retailer is closing the gap between in-store and online purchasing by merging shopping experiences across its mobile app, website, and brick-and-mortar stores.
4. Personalized Shopping Experience
Take a look at how e-commerce websites function – bringing customers exactly what they need, every time, on every device, without fail. This carefully curated shopping experience eliminates the need for buyers to browse through the inventory of online stores to find what they need. Over time, this approach toward shopping has been normalized to an extent that customers expect the same out of their retail shopping experience too. Installing smart screens, tablets etc. is one way of using technology to recreate the same sense of personalization in your retail business.
5. Banking on Data
Big data is the next big thing in terms of business operations. Multinational corporations are pumping in billions of dollars to assimilate and organize this seamless information to create the right kind of marketing strategies. While big data may be out of your reach as a standalone business entity, you can create your own pool of data and use it to offer improved retail experiences for your customers.
Fun quizzes, for instances, are a great way to gather insights into your customers’ buying preferences, which can then be used to offer personalized product recommendations. You can take it a step further by tracking these recommendations to know if they are appealing to your customers and tweak them accordingly.
Related Reading: How Big Data and Analytics are Evolving Customer Experience in Retail
[Courtesy : European Bank for Reconstruction and Development]
Meanwhile, other technologies like virtual and augmented reality will continue to grow in popularity and efficiency. As a retailer, the onus of using these disruptive innovations to offer a seamless customer experience falls on you. Pairing with the right technology partner is the first step. Get in touch with our experts today to uplift your retail experience with cutting-edge software solutions.
Gone are the days when marketing in retail, meant hours of trial-and-error based planning and strategizing. Marketing is no longer reliant on the marketer’s gut feelings or intuitions. It is moving away from being an art and is becoming more of a science now.
One of the major reasons that can be attributed to this shift is big data. Yes, the endless amounts of data that are being collected by businesses on an on-going basis are changing the game for retail nowadays. Business enterprises that leverage this data through analytics, and uncover useful insights are able to make their marketing plans more effective.
Isn’t it great when you are able to analyze what kind of products your customers are likely to buy, or what is the highest price that a customer of yours is willing to pay for a particular product? You get to channel your marketing efforts in the direction which is most likely to give you better profits with such information.
With predictive analytics, you get to garner all the insights necessary to personalize your marketing efforts for customers, making it much more effective and useful.
So here are some of the areas where you can use predictive analytics for personalization:
Customer engagement and revenue
Predictive analysis helps to identify the different ways in which customers engage with retail sites. This information can be used to drive the desired level of engagement from the customers.
There are several solutions available in the market that can help you figure out or track customer behaviour. You can use those solutions to get a better understanding of what your customers are like, and have them adapt to your business model as your objectives evolve.
For example, retail giants like Amazon and Netflix, use predictive analysis to examine customer behaviour and develop solutions for their sales team to earn better and more qualified leads.
Amazon makes use of customer’s past purchases, details about their virtual shopping cart items, the items they have liked or rated etc. to decide and offer future product recommendations.
Netflix makes use of ratings made by customers on TV shows and movies to offer additional movie and show recommendations.
This way, predictive analysis tools help a great deal in obtaining information which when combined with a company’s already existing customer base, enables better and more effective marketing.
Better focused promotions
Promotions are every retail company’s best friends. But to get them right and to get leads out of them, you need to make some serious effort.
Studies say that almost 98% of fast growing companies feel that targeting and market segmentation are extremely important for online merchandising but more than half of them are not completely satisfied with their promotional tools.
Predictive analysis can be used to avoid such situations and devise personalized promotional strategies that work for a particular customer or a particular segment, by combining data collected from various sources.
For example, Macy’s used a predictive analytics solution that focused on targeting registered users of their website and within three months, they saw an 8 to 12% increase in online sales. They used information related to browsing behavior and combined it with product categories to send out targeted emails to each customer or market segment.
Similarly, another retailer StitchFix sends out a style survey to customers, on the basis of which the customers are given recommendations on the clothes they might like, using predictive analysis.
Inventory management
Predictive analytics can be used in inventory management as well in order to prevent out of stock situations and to reduce overstock.
One retailer that revolutionized inventory management by introducing a system of Vendor Managed Inventory (VMI), was WalMart. They made use of predictive analytics to take it to an all new level, whereby they could reduce the inventory threshold for a product if the solution predicts no immediate sales for it. This allowed them to allocate their resources on products that are greater in demand and have the potential to increase profits.
Customer service
Many retailers face issues in customer service relating to whether or not they need phone service, if yes the number of executives required for phone-based support, live chat services, prioritizing questions from customers and the like.
Predictive analysis helps to set this line straight, by building a model that specifically meets the needs of the retailer. Over time, the model or the solution can be refined and modified for more accurate predictions and improve overall customer service.
For example, Red Hat which is a Linux distributor uses predictive analysis to enhance customer service by increasing “subscriber stickiness”. With their solution, they were able to provide solutions to customers, for problems they didn’t even know they had.
Hotel chains like Marriott also use predictive analytics tools with the aim of exceeding their customer expectations at all times.
Apart from these, there are many more areas where you can use predictive analysis.
However, merely using a predictive analytics solution and dumping data in is not enough. They are not plug and play solutions, that take data in and generate revenues. You need to work with skilled data analytics personnel to make sure that your investments in predictive analytics are not wasted. You need analytics experts to make the most out of big data. Once you have the right solution as well as the right personnel in place, then you are not far away from effective marketing.
What aspect of our lives has technology not influenced right? We literally have almost everything in our lives automated and at our convenience, from home delivered groceries to mobile banking facilities. Our health too, is not left behind. With Electronic Health Records (EHRs) and other healthcare applications, there has been quite a few milestones in the healthcare industry as well.
For instance, we have wearables that allow users to monitor their heart rate or keep track of all kinds of health metrics while they are working out. We even have smartphone adaptable glucose meters for diabetics and many such other devices developed for our well being.
So, we’ve seen the beginning of technological advancements in healthcare, now where is it headed? Can we imagine a world where patients can probably be warned about a looming heart attack or an insulin shock for diabetics? With the help of all the health data collected from these wearable devices over a period of time, it could actually be possible, although we’re not quite there yet.
The road definitely seems to be leading us to that. A significant change needs to take place in the way healthcare operates currently for that world to become a reality and the good news is, it has already started!
Here are the top 5 technology trends in healthcare that are likely to create the next big revolution in the industry:
- Big data – Just like any other industry, the influence of Big data is pretty significant in healthcare as well. As healthcare is becoming more automated or digitized, the data that is collected in the process, through various points, is obviously huge, and the need to act on such data seems to be the new focus. Hence, organizations are looking for ways to store, access, as well as, garner useful insights from the data. It is a good start towards the goal of being proactive in our treatments and predicting outbreaks. It also helps in understanding diseases better and personalizing medicines and treatments, in order to improve the overall quality of care. It could even help in preventing pandemics.
- The cloud – Organizations are making more use of the cloud because of its typical benefits like lower costs, scalability, and anywhere-anytime-accessibility. It also enables easier and faster implementation of new initiatives allowing all users of the data, like patients, providers and doctors to access it anytime they need. For organizations where full-fledged cloud infrastructures are adopted for all their processes, they can save a great deal on hardware and software maintenance costs, which lets them free up resources for more important projects.
- Self-sufficiency – With the advent of more and more technologies designed to engage patients, like wearables, health metrics tracking apps, patient portals and other such developments, people feel more empowered and self-sufficient to take ownership of their own health. Such devices also help in generating data that can probably play a major role in delivering personalized medicines and treatment. Once such data is integrated into our healthcare systems, with course of time, it will lead to the growth of personalized healthcare.
- Interoperability – The healthcare system of any organization needs to connect with external entities and systems for various purposes. Now, a basic level of such interoperability is already accomplished. The focus now is on bringing about a more advanced, safe and secure level of seamless interoperability between connected systems and external sources to provide a comprehensive view of a patient’s health. This again helps in collecting meaningful data that can be used for proactive health care in future.
- The pharma and healthcare – The collaboration between the pharma and healthcare has always proven beneficial for both industries. However, due to security concerns and different data standards, it has always been quite a challenge. But now, with significant improvements in the IT sector in security and cloud computing, the impact of these impediments is reduced and both industries are able to work and collaborate more freely with sufficient transparency in their processes. This also helps in providing data, such as drug trial results, genomics research, medical images and the like to different users in different industries, which can further improve the quality of healthcare as well as pharma products.
It’s basically all revolving around the data, that is, collecting more data, extracting insights from the data and doing more with the data that is captured. Hence, organizations not only need to look for more ways to work with all the data, but they also need to make use of full-fledged data platforms that help in the management and real-time integration of data, in a safe and secure manner.