With the shifting key values of every industry from customer-focused to customer-centric, there is an immense growth in data and information. This has changed customer dynamics and as a result, every business is facing numerous challenges such as market uncertainties, driving efficiency, and effectiveness in their marketing productivity, immense competition, customer demands, fraud detection, and risk management.
Many businesses across all industries, in order to cater customer demands, are trying to utilize the data that their customers leave behind while interacting with the company. Harnessing this pool of data can offer several benefits to organizations. However, many companies still have not realized the importance of data mining and have not gone beyond gathering and storing their data. Although it is difficult to deal with an unstructured set of data by leveraging predictive analytics solutions businesses can fetch optimum results from such data.
At Quantzig, we understand the impact that predictive analytics solutions can have on your business. And to help companies derive actionable insights from large and complex data sets, our team of experts has highlighted three important ways in which predictive analytics solutions can help in managing large volumes of data and setting up analytical frameworks to derive real-time insights that facilitate more informed and wise decisions.
The food industry is one of the most profitable sectors across the globe. However, with the increase in global competition, companies in the food industry have started facing numerous challenges such as reducing supply chain waste, optimizing supply chain efficiency, boosting sustainable growth, implementing green policies, and finding a competitive advantage. To deal with such challenges, businesses in the food industry need to focus on leveraging big data and analytics to keep close tabs on new supply chain trends and competitors’ market progress.
Inefficient supply chain leads to low-profit margins and more wastage. Furthermore, if the supply chain is not optimized, it can result in the inability to adapt quickly to a problem and can increase operational costs. Therefore, players in the food industry must leverage the benefits of technology like big data and analytics to gain better insights into the ability to track and monitor supply chain activities in real time.
At Quantzig, we understand that to ensure the efficiency and performance of your supply chains, from sourcing to manufacturing to delivery, optimization is required. And to help companies in food industry thrive in the competitive landscape, our team of experts has highlighted five ways in which big data and analytics can help companies in the food industry to become market leaders.
From fitness and activity trackers to “smart clothing” and smartwatches, the possibilities are practically limitless within the industry for wearable technology. And this growth is further enhanced by the promise of high monetary rewards. But design and data collection are only a small component of the potential that wearables offer to both companies and consumers. Taking wearable technology to the next level of usefulness requires companies to build in additional analysis features that increase engagement levels, improves the usefulness of the wearable technology, and provides an unparalleled experience to the consumer. In other words, the benefits of data analytics can help companies to effectively determine what the wearable technology is actually doing for their consumers. But the question that arises here is, what are the benefits of data analytics and how can it help wearable technology to become even more popular. This article has answers to this question. Here, we have discussed in detail some of the significant benefits of data analytics that have potential to take wearable technology to the next level in 2019.
Benefits of Data Analytics
Detailed insights from collected data
One of the most crucial benefits of data analytics is that it can turn collected data into the foundation needed for actionable insights, and in doing so provides additional company and consumer benefits. For example, a sleep tracking device might collect data on how and when a consumer is sleeping. But without the analysis of the collected data, the device only helps in identifying the hours that a consumer sleeps or fails to reach a REM cycle. Does this knowledge help consumers in any way? The answer is simple, no! It is in this context that the real benefits of data analytics are realized.
Personalized services based on habits
By leveraging the benefits of data analytics, wearable devices can provide personalized offers to consumers. By analyzing the data captured by wearable technologies, companies can create marketing offers that are customized to each consumer. For example, a step-tracker can reveal that a consumer takes the most steps around lunch time. By leveraging this information via personalized offers, companies can encourage the consumer to take more steps throughout the day.
Improved employee health and productivity
One of the key benefits of data analytics is their ability to improve the health and productivity of employees. Wearable technology can collect the data needed for a company to analyze the productive hours of the day of their employees. Additionally, the devices can also monitor employees’ health, so that the company can mitigate risks associated with lack of sleep, high levels of stress, and other health symptoms that contribute to an ineffective and unhappy workforce.
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Today, big data analytics is one of the most crucial processes for any business, big or small. For data scientists, it acts as a pair of glasses that helps them see the actual reality of a business’ performance, beyond scattered numbers in graphs. A proper, solid, and reliable analysis allows you to make fact-based and rational decisions, but if mistaken, advanced data analytics can lead you astray and you might end suffering a huge loss. So, it can be safely presumed that it is not enough to have good quality data unless you use the datasets efficiently. However, there are many hurdles that businesses might encounter along the way. While implementing a new strategy to strengthen your business with advanced data analytics, mistakes can prevent you from realizing its complete potential. So, in this article, we have summed up some of the common blunders businesses should avoid while developing a big data analytics strategy.
Mistakes to Avoid in Big Data Analytics
Being rigid in processes and products
If you are rigid with your process and product, you are committing a big mistake. You should begin your project in a way that is both strategic in vision and agile in execution. Therefore, you need to pick technologies that are open and expandable. For example, you must avoid vendor lock-in by using open source tools. For obtaining optimum results from advanced data analytics, it is important to foster a culture that fosters failing fast and learning from mistakes. You must avoid letting egos drive your project and understand that if your team experiments on ten things, eight of them might not work. You should get people on board in your data project team who can thrive in this sort of DevOps style of work.
Neglecting security and governance at the beginning
Today, security and governance are extremely important, as privacy is one of the major concerns in almost every industry. Businesses still tend to start big data analytics projects as pilots, with just a handful of people working on them, and without security and governance baked in. This is a huge mistake when it comes to big data analytics. You must get compliance, governance, and security conversations started on the very first day of the project. You must carefully choose the right governance strategies, as well as the right technology of governance.
Relying on the same KPI’s always
As things are constantly changing and your business is subjected to constant dynamics, so you must learn to adapt to the ever-changing environment. This is how you can prosper. So, try not to hold on to the old performance indicators that are used to measure your success in the past. You need to use newer and more suitable tools to make advanced data analytics tools reflect the current performance of your business and identify what really drives your business forward.
Being a leader in offering big data analytics services, Quantzig helps businesses to manage, store, and integrate huge datasets. Also, we help businesses to gain predictive insights that facilitate proactive business decisions and pre-emptive planning. Additionally, Quantzig promises to deliver best-in-class frameworks for multi-dimensional data aggregation and utilizes visualization-based data discovery tools for insight generation.
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Food waste management is a matter of global concern. According to recent estimates, roughly one-third of the food produced in the world for human consumption every year — approximately 1.3 billion tons — gets lost or wasted. This contributes to the emission of greenhouse gases from landfills. Food waste occurs across the entire food system ranging from producers, retailers and restaurants, to consumers. Forward-thinking businesses are using advanced technology such as data analytics to tackle the issue.
Using big data and data analytics to collect real-time operational data throughout the food waste disposal process allows visibility into the organic waste stream. This further facilitates businesses to identify inefficiencies in food management processing and helps initiate process improvements to create immediate impacts. Measuring and optimizing food waste management not only supports environmental directives but also forms the key to finding operational efficiencies, enabling a business to make informed decisions about purchasing, production or other logistical needs.
How can data analytics help in food waste management?
Retailers and other businesses dealing with food products are increasingly turning to data analytics solutions in order to manage the food wastes. The information collated and analyzed using data analytics reveals the waste generated by the business and seasonal change in demands, helping business to better plan their food waste management strategy.
Optimum inventory level
Analyzing sales information, weather forecasts, and seasonal trends, help manufacturers to identify an optimum inventory level which they can then use to reduce the effects of food wastage. Predictions of consumer demand during a particular time can then be made and promotional plans and sales approaches can be structured around sell-by and expiry dates. This is primarily intended to cut down the food wastage and the knock-on environmental and cost issues that arise.
Predict changes in demand
Data analytics can be used to identify seasonal changes in consumer demand for food products. This helps retailers or restaurant chains to plan what quantity of a particular food item must be produced or procured, consequently leading to reduced wastes and better food waste management. Data analytics also helps retailers determine the products that are closer to expiry and aggressively reduce the prices on such items so that they are consumed before their expiry date and not wasted.
An example of analytics in food waste management
A notable success story for using analytics in food waste management is that of the British multinational groceries and general merchandise retailer – Tesco. The company uses a data-driven approach to reduce food waste and ensure effective food waste management. Tesco’s systems order approximately 110 million pounds of food products every day. So, the retailer turned to data analytics to improve the supply chain and minimize the instances of food wastage. Their systems utilize large amounts of data from its many store locations to develop, train, and test their algorithms. They utilize weather forecasts to increase their accuracy in predicting how the demand for food will change. Common sense tells you the seasonal change in people’s demand patterns. Data tells you exactly how much the change is and plan the inventory accordingly. This method helps minimize food waste by ensuring the right quantity of food products are available at each location. In addition to reducing waste and ensuring better food waste management, these initiatives have a positive economic impact for the retailer as well.
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Today, transportation and logistics (T&L) companies have embraced advanced data analytics in their operations since it allows them to run sophisticated networks globally. But unfortunately, the investments made in advanced data analytics is not satisfactory. Now, it is time for that to change if the companies in the T&L sector really want their sales performance to grow. The commercial analytics capabilities of this sector lag average performance, which is primarily because of the reason that sales forces in this sector rely heavily on outdated processes and lack proper insights into preferences of their customers and growth opportunities. Here rises the need for big data analytics for T&L companies. They already have enough data and can utilize big data analytics extensively to earn desired outcomes. With years of experience in offering data analytics solutions to businesses, we have noted that companies in this sector who embrace advanced data analytics can generate an additional 2-5% percent return on their investment. In this article, we have summed up three things that T&L companies need to do to realize the true potential of advanced data analytics:
In this age of digital intelligence, procurement data is no longer restricted to spend data. Now, data from multiple sources including both internal and external is easily accessible to the procurement team. There is immense opportunity to deploy advanced analytics on this combined data to understand market dynamics, purchasing trends, behaviors of business stakeholder, and team performance. The derived insights can be used to brace future procurement decisions and drive value for the whole organization. Moreover, today procurement managers need to assess their real position on the ‘analytics value chain’, to get a true picture of where they actually stand. Once they gain clarity on this, actions can be taken to move ahead based on the level of maturity. The analytics experts at Quantzig have identified four metrics through which procurement analytics can help businesses reduce overall cost base and achieve more for less:
How can procurement analytics help your business?
Procurement analytics can help to optimize the working capital. Furthermore, it helps in controlling spend over budget by comparing the purchase order value or the budget with the actual invoicing. Procurement analytics also aids in analyzing “Incoterms” to identify cost-effective opportunities.
#2. Pricing metrics
Procurement analytics helps organizations to understand whether they are paying different prices for a similar service or product across its geographies and divisions. It helps identify price variance by division or geography. Furthermore, it also helps analyze spend or price development to assess whether there is an increase in spending for an SKU or category and if it is resulting in a per unit cost reduction.
#3. Compliance metrics
Procurement analytics helps organizations to identify any variance from the agreed KPIs and defined process. Typically, these cannot be avoided completely but can be definitely controlled as they always come with a higher price point. Moreover, procurement analytics identifies spend from non- preferred and unapproved suppliers. Also, it aids in fraud detection by analyzing factors like spend near approval limits and large spend without a point of sales.
#4. Supplier base metrics
Procurement analytics helps organizations to understand the existing supplier base’s performance and identify opportunities to integrate further by using more global contracts and secure better pricing. Furthermore, procurement analytics helps to analyze the geography or division that has a high supplier base built up over the years. It aids in leveraging the geographic reach of existing suppliers to secure global competitive rates.
If you want to employ robust procurement analytics and uncover better insights from data for better vendor management, negotiation tactics, and purchasing strategy- request a free proposal now!
There are several questions asked by healthcare professionals like – Are there enough measures available that must be taken in case of flu? Or are the patients operated yesterday likely to catch an infection? How can recruiting staffs and providing facilities be done in a cost-effective manner? These questions can be answered accurately using advanced analytics, which, in turn, can help in reducing health care costs. As the number of patients keeps increasing along with the associated costs, there is a dire need for adopting advanced analytics in healthcare. Advanced analytics has become a tool in reducing health care costs for many healthcare organizations. It can help in reducing health care costs through various segments, ranging from stock management to patient care to staff deployment. But before delving into the correlation between advanced analytics and health care, let’s first understand what is advanced analytics.
What is advanced analytics?
There is a simple difference between analytics and advanced analytics. Traditional analytics is used to get insights on the current happenings; whereas, advanced analytics helps understand the future to forecast upcoming behaviors and trends. This tool can be categorized into data mining and big data. Advanced analytics uses various mathematical techniques and statistical modeling techniques to analyze current and past data and predict future scenarios.
There are several ways in which advanced analytics can aid in reducing health care costs. Predicting demands of operating rooms, reducing the rate of readmissions, adding intelligence to pharmaceuticals, and optimizing staffs are some ways in which advanced analytics can help reduce costs.
#1 Predicting demands of operating rooms
Operating rooms are quite expensive to maintain. So, every hospital tries to optimize the operating room without compromising on patients’ health. This goal can be accomplished by recognizing the role of advanced analytics in better understanding the relationship between the operating rooms that can lead to mismanagement of effective scheduling. Thus, advanced analytics can help in streamlining the operating room schedule and reduce health care costs.
#2 Reducing rate of readmissions
Unnecessary readmissions are very frequent in the U.S and it leads to confusion of discharged patients who fail to understand how to take care of their health or take precautions after they get back to their home. Due to this, an unnecessary burden of cost is also created. This is where advanced analytics comes into the picture.
Advanced analytics helps in reducing health care costs effectively. New advanced analytics algorithms take into account various clinical factors, which helps identify patients who need to spend less than two nights in the hospital. This tool also helps doctors to know when a patient requires observation and, thus, helps in reducing health care costs to a large extent.
This is one of the most powerful features of advanced analytics since it can helps investigate every corner in detail and unveils available opportunities and forthcoming challenges. The historical data available in the clinics and hospitals can help in creating predictive models that can subsequently help the pharma companies to respond to the expected and unexpected changes. Advanced analytics can also be used to uncover the opportunities for internal savings caused by inventory standardization and, thus, help in reducing health care costs.
#4 Optimizing staffs
Advanced analytics can help in trimming costs of labor and predict demand in advance to match resources and staff; thereby, minimizing the last-minute unnecessary expenses. Optimizing the staff skill using advanced analytics can be of great help, especially in reducing health care costs.
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Most organizations collect employees’ data in one form or the other and utilize it for planning and transforming their business structure. But managing human resources data of an organization is no easy task. The use of analytics in HR operations over the years has made the tasks much easier for HR professionals. Analytics also help companies gain strategic insights and develop the ability to model how workforce trends impact revenue and profits — quickly and accurately. The use of analytics in HR also proves to be beneficial for the employees as it increases employee engagement in an organization. However, there are several roadblocks that would limit companies from exploiting the full potential of HR analytics. Let’s look at what they are:
It is necessary to collect and organize data from various operations and departments within the organization for successfully implementing analytics in HR. Data has to be acquired, sanitized, unified and analyzed from multiple departments as well as from multiple business functions, including payroll and finance. Therefore, companies need experts who can not only analyze the data but also gather and organize the right data.
Lack of data analytics skills
Though companies are intensively promoting the implementation of analytics in HR functions, the hard truth is that the analytics skills of most HR professionals are limited. Most of them also require adequate training to become well versed with the art of converting data into meaningful insights. This often makes the successful implementation of analytics in HR a difficult and complex task in most companies.
Privacy and compliance
Analytics requires an adequate amount of data to be collected from various reliable sources to produce the desired results. While gathering data about an employee or a potential employee, especially from external sources, HR professionals must consider privacy. Gathering personal details of employees could sometimes land the company in trouble.
Insufficient IT resources
The implementation of analytics in HR is an IT-intensive process. Many companies, especially smaller companies do not have the infrastructure required to set up an analytics program. Furthermore, setting up the required infrastructure could prove to be an expensive and time-consuming affair for companies. This is one of the main reasons why several organizations refrain from implementing analytics in HR processes.
HR has a variety of tools for various services sourced from different vendors. However, in most cases, these tools work in isolation. This proves to be a major challenge for organizations. To positively make use of analytics in HR, companies will have to aggregate these silo systems which would prove to be a difficult task in itself.
Companies often face flak from HR professionals while relying on computers to undertake HR functions, especially in cases like hiring. They tend to feel that it takes out the “human” factor from “human resources”. There are also chances that the analytics systems might not always be accurate in predicting the right outcomes.
To know more about the trends in implementing analytics in HR