Tag: Data Analytics

metadata management

A Definitive Guide to Metadata Management

METADATA MANAGEMENTWhat is Metadata Management?

Metadata is like a golden thread that interconnects different functions of an organization. It comprises heterogeneous, heavily distributed and hybrid mixes of data from cloud systems. Metadata management deals with the management of complex data through analytics, operations, discovery, collaboration, and compliance. Metadata management acts as a key enabler in the data-driven decision-making process of a business. For any organization, all the data-driven actions pass through metadata when the customers browse through the website, run a query or make a virtual view.

Businesses need to understand and analyze their data to survive and grow. Metadata management can provide the required processes to gain this insight and succeed. Want to know how? Get in touch with our experts.

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Why is Metadata Management Important?

With the growing volume of data, metadata management has gained huge importance. Although it sounds a little geeky, it is a crucial component of records management and data governance. Here are a few reasons why metadata management is important:

Reason #1: Increases the longevity of datasets

The lifespan of a particular dataset can be very short due to the unavailability of relevant metadata. The growing complexity of data with the increasing constraints of time and funding also contributes to the shorter life span of the datasets. But metadata management can counteract this tendency of data degradation efficiently. 

Reason #2: Facilitates data reuse and data sharing

Metadata management ensures that the complex and complicated data is easily analyzed, interpreted and shared. It provides detailed instructions to interpret unfamiliar data accurately and helps in data integration.

Reason #3: Helps in maintaining historical records of long-term datasets

The datasets may encounter inconsistency in methods and instrumentation during the ongoing long-term projects. Metadata management helps in maintaining the historical records of such long-term datasets, thereby assisting in data governance. Furthermore, metadata management is important for data integration that contains different parameters but shares common temporal or spatial domains.

Wondering how metadata management system can enable you to build an enterprise model based on the metadata developed from various attributes such as your data integration tools and data warehouse? Request a FREE proposal now!

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Reason #4: Assists in improving interoperability and integrating resources

Metadata management makes it easy for humans as well as machines to understand the different resources. Also, it facilitates the understanding of how data is exchanged among many systems with different data structures, operating systems, and interfaces.

Reason #5: Protects resources and their future accessibility

The digital information today has become very fragile and is susceptible to corruption. Metadata management helps in archiving and preserving such information by tracking the lineage of the information and describing its characteristics. Also, it helps in understanding the behavior of the information to prevent its replication in the future.

Steps to Effective Metadata Management

Step #1: Meta repository creation

There are several repository tools available in the market that profess to create metadata repository. But all such tools may not suit your business. Most of these tools focus on one specific aspect of metadata. Therefore, you need to collect a set of metadata about each element of your business and relevant to your organization in the following six categories:

  • Business metadata
  • Core and technical metadata
  • Data quality metadata
  • Search metadata
  • People metadata

Step #2: Identify core attributes

Develop key business queries to get a fair idea about the underlying attributes which is necessary for analysis. It is important to identify and understand such attributes as they play a crucial role in downstream analytics.

Step #3: Acquire the metadata

Once your foundational capabilities are established, the next step that comes is implementing the meta acquisition process. Most of the categories of metadata need proper processes for acquiring it from the people who work in various functions of the organization.

A properly built metadata management model has the potential to give a 360-degree view of how operating systems in your organization are connected together. Such models can help users to solve issues based on different attribute types and names. Request a free demo to know more about how metadata management models can help in creating custom metadata types.

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Step #4: Create a protocol and maintain consistency

Data keeps on changing constantly with the growth of new business initiatives every day. Such changes result in long-term data instability. Therefore, it is necessary to create a more efficient process to monitor data streams for change. This is the most difficult but important step in establishing an effective metadata management process.

Metadata Management Vs Master data Management

Metadata management talks about the content in the data that is gathered and helps the organizations in uncovering the facts hooked to their data. Through metadata management, users can decode the data in terms of what it represents and derived. Metadata management consists of all the entities and attributes, programs, calculations, reports etc. The nature of metadata depends on what it talks about.

Master data management talks about the data that consists of entities like products, services, people, organizations etc. It usually talks about non-transactional data which usually covers:

  • Internal entities such as employees, departments, and products
  • External entities such as suppliers and customers
  • Information of organization such as reporting hierarchies and the chart of accounts.

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fraud analytics

Reasons Why Fraud Analytics is the Need of the Hour

What is Fraud Analytics?

Fraud analytics is an umbrella term that comprises numerous quantitative sciences like business intelligence and data analytics in order to better understand fraud. Furthermore, fraud analytics helps in developing effective fraud detection solutions through data science.  Many organizations practice traditional anomaly detection methods for fraud prevention and detection. But unfortunately, they are not powerful and function under certain limits. When analytics is added to such traditional fraud prevention methods, it improves the capabilities of fraud prevention and gives a new dimension to the fraud prevention techniques. Additionally, fraud analytics empowers organizations to have proper control over every ongoing activity.

Reducing exposure to risks, fraud, and credit losses is a daunting task for businesses. Are you facing the same issue? Get in touch with our experts to know how our fraud analytics solutions can help.

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Fraud analytics can prevent businesses from the cost of fraud which is equivalent to a financial iceberg, some of the direct losses are clearly visible but there is a huge mass of hidden harm that companies cannot see.

Why is Fraud Detection Important?

For an organization, fraud detection forms an integral part of the risk analysis process. It provides the ability to quickly filter out fraudulent sources which prevent an organization from any high-level risk in the future. Here are a few reasons why fraud detection is important:

  • Decreases exposure to fraudulent activities
  • Reduces costs associated with fraud
  • Identifiesvulnerable employees at risk of fraud
  • Improves organizational control
  • Enhances the organizational result
  • Boosts the confidence and trust of the shareholders

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Are you facing difficulties in financial risk monitoring and management? Leveraging fraud analytics solutions may help. It has the potential to manage risks more effectively to maximize ROI. Request a free proposal to know more about our portfolio of fraud analytics solutions.

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Benefits of Fraud Analytics

Helps in identifying hidden patterns

Fraud analytics along with machine learning has the potential to help organizations in identifying new trends, patterns, and scenarios under which frauds take place but traditional approaches to fraud prevention miss such things.

Aids in integrating data

Fraud analytics plays a crucial role in integrating data. It combines data from public records and multiple sources that can be integrated into a model.

Improves existing efforts

Fraud analytics instead of replacing traditional rule-based methods just adds up to existing efforts of the organization to provide improved results.

Harnesses unstructured data

Unstructured datasets are the most complex thing for companies to deal with. Fraud analytics helps in deriving the best value from such unstructured datasets. Data warehouse of any organization consists mostly of structured data, but unstructured data is the source where more fraudulent activities take place. This is where fraud analytics plays an important role in reviewing the unstructured data and preventing fraud.

Our recent success story highlights the importance of fraud analytics in the telecom sector and explains how leveraging fraud analytics solutions helped a telecom industry player to overcome revenue losses. Click here to read the full story.

Methods of Fraud Analytics

Perform SWOT

With the increasing importance of fraud analytics, organizations too have realized the important role it plays in the process of growth. But many of these organizations are opting for expensive fraud detection solutions that do not match with the strengths and weaknesses of the company. Therefore, it is essential for the organizations to do SWOT analysis before starting with the fraud detection process in order to make it work to the fullest.

Sampling

Sampling is an effective method of fraud detection that involves a lot of data. However, it has its own disadvantages too. Sampling may not be able to control the fraudulent activities completely as it takes only a specific population into consideration.

Ad-Hoc

Ad-Hoc is nothing but identifying fraudulent activities by the means of a hypothesis. It offers the ability to explore and test the transactions in order to detect the fraud. Also, you can conduct a hypothesis to test and find out if there is any abnormal activity occurring and then you can investigate the same.

Repetitive or Continuous Analysis

Repetitive analysis generally refers to creating and setting up scripts to run against a huge volume of data to determine the frauds as they occur over a period of time. This method of fraud analytics helps in running the script every day to go through all the transactions. Furthermore, this provides periodic notification regarding the frauds. This method can help in enhancing the overall efficiency and consistency of your fraud detection processes.

We understand the challenges companies face in the management of claims, underwriting, actuarial, pricing and marketing functions. Our analytics solutions provide best-in-class frameworks to determine the level of risk in claims, creating individualized and relevant policies to suit and benefit customers, and identifying key indicators that provide insights into risks and quantify predicted loss severity from loans and claims. Request a free demo below for more insights.

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healthcare industry

Quantzig’s Data Analytics Engagement Helps A Leading Healthcare Industry Player Realize Over 5% in Profits and Improve Customer Engagement

2018 has been the year of emerging healthcare industry trends and it is expected to transform the industry in the years to come. This year, however, companies will witness rapid changes with transparency becoming one of the key concerns for healthcare providers. Startups of today are bringing innovative changes to the market by combining technologies like AI and machine learning with traditional practices.

With continued vertical integration, mergers and acquisitions, and big tech companies making their move to the healthcare industry, it’s becoming clear that the pace of disruption in healthcare is accelerating. Moreover, newer healthcare industry trends and the advent of technology have brought the healthcare industry to the zenith of modernization and innovation. But the constantly rising competition in this sector calls for healthcare companies to generate situational-driven strategic insights, cope with upcoming healthcare industry trends, and act with deliberate speed to acquire more customers and retain the existing ones. As far as the healthcare marketers are concerned, it is high time that they identify the ongoing healthcare industry trends, rethink underlying assumptions, and re-engineer their approaches to marketing to stay relevant in the market and create a better customer experience.

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healthcare data analytics

4 Effective Ways Healthcare Data Analytics is Reducing Healthcare Costs

Despite its promising growth prospects, healthcare service providers are hard pressed to find solutions to multiple complex issues including regulatory and policy changes, medicinal and technological advancements, rising costs, staff, and trained employees, maintain efficient operations and services, and support other healthcare initiatives. With increasing concerns for living healthier, longer, and lead more active lifestyles, healthcare costs have increased. Research reveals that the spending and healthcare costs often rise at rates more than the rate of inflation and is expected to increase even more in the years to come.

With the rising healthcare costs, co-pays and deductibles have become expensive and employers are burdened to take a bigger cut of their employees’ wages to pay for insurance premiums. This surge in healthcare costs will soon become a big barrier to the growth of the overall healthcare industry. Therefore, leaders must find alternative methods to combat rising healthcare costs. They must do the appropriate research to find funding, grants, and contributors to help them conduct research, set up programs and implement processes at the pace of change.

At Quantzig, we understand the impact that costs have on your business plans. And to help companies excel in an ever-competitive marketspace, our team of experts has highlighted four effective ways in which healthcare data analytics can reduce the Contact USrising costs of care.

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metadata management

3 Common Mistakes to Avoid in Big Data Analytics

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.

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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 carefullRequest Proposaly 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.

Quantzig’s Advantage

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

Zero Waste Solution: Effective Food Waste Management with Data Analytics

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.

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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 rRequest Proposaletailers 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.

               

Know more about Quantzig’ s solutions for the food and beverage industry

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metadata management

3 Ways Advanced Data Analytics Can Drive the Next Wave of Growth for Transportation and Logistics Companies

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:

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retail analytics

4 Ways Procurement Analytics Can Help Add Real Value to Your Business

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?

#1. Financial metricsContact US

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.demo

#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!

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Big Data

Top 3 Reasons Why Every Business Needs a Big Data Analytics Strategy

Change is inevitable and adapting to change is the only way companies can sustain themselves in a competitive marketplace.

With advancing technology, business processes face the need to be regularly updated and analyzed to boost efficiency. Technology has made data one of the most important assets for any company. It is one of the keys that can boost the competitive advantage of one business to a great extent. In fact, if you are looking for ways to boost the productivity of your business, then maximizing the amount of data being analyzed is one of the most crucial strategies for success. Speaking of data, one of the hottest trends in data analysis this year has been big data. By utilizing a big data analytics strategy, companies can easily develop an effective strategy for their business’ successContact US by analyzing their unstructured data. In this article, we have highlighted the key reasons that make big data analytics strategy important for enterprises. (more…)

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