Data literacy – the skill growing enterprises must watch out for!

Data is the most critical of resources that an enterprise can possess; second only to its people. And while it’s great that data also happens to be the most abundant resource in today’s time, especially the growing enterprises, very few businesses are able to use it to achieve tangible results.

That’s because most people — including many business leaders — lack data literacy, the ability to make sense of data and turn it into actionable information. As a result, enterprises are unable to translate data into action and ultimately, into results.

The companies that do understand data and drive their decisions based on data analytics tend to outperform businesses that don’t. For example, a Mckinsey & Company study suggested that analytics-driven businesses were able to acquire 23 times more customers than those that didn’t use data. To be able to use data to achieve such results, data literacy is an absolute necessity.

An increasing number of growing enterprises are leveraging technologies like analytics, AI, and IoT. Since these technologies thrive on data, a data-literate workforce will be able to extract the most value out of them. However, most businesses find it hard to build a data-literate workforce. That’s because making sense of data, which is often generated in the form of statistical tables and graphs, can be challenging for multiple reasons.

Why it can be difficult to make sense of data

As mentioned above, data is the resource that is abundantly available to enterprises. And more of it is created every passing second. The sheer volume of data generated by enterprises from various sources can prove to be overwhelming for non-analysts. That’s because they are not trained to know what to specifically look for in the ever-growing body of data.

They may not know the right questions to ask and where to look for answers, and as a result, they attempt to go through all the data that is available to them. Result? These workers lose a lot of time getting nowhere, and even worse, end up making decisions that are not based on sound facts.

Another challenge to understanding statistics is the inability of people to interpret different pieces of information coherently. This is why, while enterprises can understand statistics in silos, they’re unable to identify how the given information is relevant to their company, their function, and the problems they are trying to solve.

Furthermore, despite having piles of reports filled with detailed statistics, people may not know how the data is collected, analyzed and how certain values are calculated.

For instance, many people do not know the difference between mean, median, and mode, which are different ways to generalize data and determine averages. Each of these is determined differently and used in different applications, but often ends up being misunderstood during data analysis. Similarly, people can also misinterpret percentages and probabilities due to a lack of data literacy and therefore arrive at erratic decisions.

To ensure that employees always make the right decisions, it is essential for business leaders to foster digital literacy in the enterprise.

How enterprises can foster data literacy

Building a data literate culture entails taking a multi-faceted approach that goes beyond just educating employees. Enterprises should also update their policies and technology to augment their move toward data literacy, as follows:

Promoting data-driven practices

Businesses should cultivate data literacy as an inherent part of their organizational culture so that not just analysts but the entire workforce can use data to make decisions, big or small.

To do so, businesses should encourage employees to adopt a data-driven approach towards solving problems and also demonstrate its application in different functions. Special training in data literacy can go a long way in making the organization fully data-literate.

Making data available and accessible

The most important step towards achieving total data literacy in any enterprise is by making data available to all the employees of the organization. Also, it is vital that workers are only given the information that is relevant to them and their functions to prevent information overload.

In fact, businesses should go one step further and provide data to their employees in a format that is easily consumable. Doing so would encourage employees to readily adopt data-focused practices to make even the smallest of decisions, which can have a compounding effect on the overall business performance.

Delivering data in natural language

When it comes to choosing a format that employees can easily understand, nothing comes close to the natural, conversational language that we use every day. Realizing this, leading businesses are using natural language-based methods to deliver data to their workforce. They are using natural language generation tools that make the information in their dashboards and reports more easy to grasp and act upon.

Using natural language generation tools can help enterprises convert large volumes of data into easily consumable reports. As a result, everyone can know exactly what they need to know and do without being overwhelmed by data volume or complexity.

Achieving data literacy will soon go from being an option to becoming a necessity. Growing enterprises, thus, must accelerate their efforts to make all their employees data-literate. Doing so will ensure that two of their most critical resources — their people and data — can be used to their full potential.

Giving Financial Reports a Facelift with Reporting Automation

Artificial intelligence is a technology that requires neither an introduction nor an endorsement of its capabilities. Popular machine learning applications capable of performing unprecedented feats like beating humans at games like chess and Go to engaging in insightful debates with humans have clearly demonstrated the prowess of present-day AI. Such applications highlight AI’s capability to process large volumes of data, perform repetitive and monotonous tasks with superhuman accuracy and automate basic creative tasks like content generation via natural language technology.

For businesses, however, it is the ability of artificial intelligence to scour through huge volumes of data to find bits of usable information that holds the most value. In the words of leading AI expert, Jonathan Mugan, “If you can tell a machine what you are looking for, it can look through more data than you could read in a lifetime to find it.”

The banking and finance sector, which needs to make sense of huge volumes of data for making critical decisions, can benefit at large from such a capability. And realizing this, the sector has been quick to adopt machine learning for reporting automation to expedite and enhance its decision-making processes.

The role of reporting automation in reshaping the banking-finance sector

Documenting and reporting financial information is a critical activity in banking and financial services operations. However, generating hundreds or thousands of customized reports with uniform levels of focus and diligence is a lot to ask for from a team of humans. That’s because report creation involves selectively extracting critical information from massive bodies of data and representing it in an easily consumable format. Errors may lead to severe financial and legal consequences. And this is where reporting automation tools are making an impact on financial institutions!

Reporting Automation tools can skim through large volumes of structured data to find valuable insights. Using natural language generation technology, these insights are translated into concise written narratives that finance executives and business leaders can use to make decisions.

Using machine learning and natural language generation technology has enabled financial institutions to improve their productivity, eliminate the scope of clerical errors in their financial reports, and ensure compliance with industry standards and internal policies.

As a result, reporting automation is empowering financial institutions as well as accounting teams at enterprises to maximize their efficiency. And reporting automation is only one of the numerous applications of AI and machine learning; other services being compounding the impact of reporting automation in different areas of finance.

The impact of reporting automation on the financial industry

The banking and financial services industry involves large volumes of quantitative data, repetitive workflows, and the non-negotiable need for accuracy — challenges that reporting automation tools specialize in dealing with. Here are a few ways artificial intelligence & reporting automation are impacting the finance industry:

1. Market forecasts 

Trends in financial markets are hard to predict since a large number of variables are at play. Keeping track of these variables and their compound effect on the market can help financial advisors spot patterns in data that are indicative of specific market trends. Financial institutions and investment firms are using AI-driven reporting automation tools with machine learning capabilities like predictive analysis to monitor the existing market conditions and forecast future market trends to guide investment decisions. As a result, these institutions are minimizing their risks and maximizing their gains from their investments.

2. Content creation

Creating reports like financial statements for account holders and market insights for investors can take up a lot of time for employees of financial institutions. To generate such reports, these employees go through massive volumes of numerical data to pick out the most pertinent facts and reproduce them in an informative format for their customers. These reports usually contain data visuals like charts and graphs with an attempt to make them look interesting and understandable. But these visualizations often lack insights making it difficult to comprehend for their investors and clients. By using natural language generation-enabled reporting automation tools, these reports can be generated quickly without any error, enabling the end consumer to understand these statements with simplified conversational narratives.  Additionally, it also frees up the employee’s time which they can utilize to perform other non-routine creative and strategic tasks.

3. Customer service

Providing timely and satisfactory customer service is a challenge for banking institutions. The banks’ advisory teams are far outnumbered by the millions of customers and investors who need important information regarding their financial accounts and investments every minute. In addition to having to respond to a multitude of queries simultaneously, the financial advisors also need to quickly access the requisite financial information and provide only relevant facts to the clients in a way they can easily understand.

Reporting automation tools can assist the advisors by automatically generating custom and personalized reports with the necessary information regarding the accounts and assets of every customer. Also, these reports can also be shared with the customers to ensure they have all the information they need without having to repeatedly contact their advisors or relationship managers.

By leveraging reporting automation and machine learning, businesses can not only automate their day-to-day operations but also enhance their strategic decision-making. This is exactly why so many organizations are utilizing AI-based tools like Phrazor to make sense of the large volumes of data generated by them and translate this data into actionable intelligence. Phrazor uses natural language generation technology to convert large volumes of structured data into concise reports with a conversational tone. Result? Businesses are able to make highly-informed, proactive decisions!

Finance and Banking Automation: Streamlining Your Reporting Process

Automation has become the focus of growing interest in the global finance industry. Many banking and financial companies are speeding to deploy the latest automation technologies in the hope of delivering the next wave of productivity, cost savings, and improvement in customer experiences.

In one of the recent studies, McKinsey observed that 60% of industries can automate approximately 30% of their manual work. With emerging AI-driven technologies like natural language generation, many of the monotonous, data-oriented tasks such as report generation can be automated providing quick results, while taking into consideration the changing regulations of the financial landscape. Let’s take a closer look at the perks of reporting automation with the following use cases.

Use Cases of Automated Report Writing in Banking & Finance Sector

1. Quarterly Earnings Report

A quarterly analysis report is one of the most important financial statements for the company. Investors assess these financial statements to determine the financial health and investment worthiness of the company. It provides a quarterly update on the income statement, balance sheet, and cash flow statement. 

Quarterly Earnings Report
Quarterly Earnings Report

Usually, the process of gathering information can be quite tedious and time-consuming but that aspect is mostly looked at by accounting software. The problem arises in explaining data in a way that every investor can understand. Why were you over/under budget? What were the contributors to performance? Today, companies have the data to make smart data-driven decisions but data alone isn’t the answer: you need to simplify data in a fashion that its consumable by everyone.

With reporting automation, companies can easily cut costs by creating accurate reports in one go using natural language generation. NLG gives you the capability to share insights in words, that can be easily understood by everyone. Automation will not only take away the mundane and repetitive part of report writing but also reduce the margin of human error increasing reliability.

2. Branch Performance Report

Branch performance reporting is a process that every level in the bank’s hierarchy partakes in. Depending on the nature of the organization, these reports are usually drafted manually by compiling & analyzing data of both their digital & brick and mortar sales. The next inline manager further adds his learnings to these reports to present it to the subsequent level. The process is not only time consuming but highly uneconomical.

Branch Performance Report
Branch Performance Report

Reporting automation has proven to reduce the overhead costs of reporting and save precious staff-hours that can be used to focus on other higher-value tasks. In this case, the branch managers can essentially focus on having client meetings and tapping onto revenue opportunities rather than analyzing complex reports. Also, manually generated reports might be prone to clerical errors leaving room for misinterpretation.

Automated report writing powered with advanced artificial intelligence like natural language generation, and machine learning capabilities helps generate thousands of comprehensive reports with personalized recommendations in a fraction of time. Considering how crucial this kind of report is to increase branch productivity & tab into new growth opportunities, it is only prudent to ensure that it is prepared efficiently.

3. Recruitment Performance Report

Recruitment performance reports are designed to track the progress of the hiring team and identify areas of improvement in different stages of staffing. Such reports provide data on various KPIs like time-to-fill, time-to-hire, source-of-hire, first-year attrition, hiring manager satisfaction, quality of hire, applicants per opening, candidate job satisfaction, and so on.

Recruitment Performance Report
Recruitment Performance Report

The major pain points experienced with recruitment performance reports is the amount of time invested in the manual processes of data extraction, data cleaning, structuring, and further analyses to derive insightful conclusions. Moreover, the lack of customizable reports for various scrutiny levels undermines the efficiency of the manual reporting process.

By automating the recruitment performance reports, companies can generate personalized on-demand reports, tailored for different levels in the organization. With just a click of the button, recruiters can fast-track the staffing process by getting access to meaningful insights with easy-to-understand, narrative-based reports.

4. Portfolio Analysis Report

A portfolio analysis report is essentially a factual summary of the investor’s assets. It helps investors to understand the status of their investments in context to the whole portfolio. As per the current industry trend, these reports are usually in the form of tables and charts lacking advisor recommendations, making it challenging to comprehend even for data-savvy customers.

Portfolio Analysis Report
Portfolio Analysis Report

Implementing automated data analytics with natural language generation technology will enable you to produce customized portfolio analysis with personalized recommendations for every investor at scale.

In addition, thanks to predictive analytics combined with NLG, even the tiniest details are captured uncovering hidden insights that may not be apparent in graphical representations.

To sum up…

Reporting automation, when strategically implemented, will increase your organization’s efficiency and productivity. It will also enable you to dedicate more resources to higher-value products and innovation.

However, choosing the right platform that has an intuitive interface and natural language generation capabilities for easy understandability and accurate reporting, is crucial for tangible improvements and employee productivity.

Personalize your Portfolio Analysis Reports for unique Customer Experience

Portfolio analysis reports are central to how your clients judge their investments and your competence. Hence, it is evident that this report should sum up every facet of your efforts and present a wholesome picture to your client. 

The intricate part isn’t understanding why a portfolio analysis report is crucial for you but understanding how to capitalize on it, how to build your client’s confidence, and how to steer them on a path of unwavering trust in your services and you.

An ideal portfolio analysis report helps your client in understanding his investments in the context of the whole portfolio. Such a report should include his profits, losses, and risks associated. The key is to tailor the report to best explain the client’s portfolio position in context to the rest of the industry.

However, it is not so easy to personalize reports for each client. Sharing personalized insights and recommendations with each retail customer is financially impractical given the magnitude of effort involved versus the returns. Thus, such premium services are mostly exclusive to HNI clients.  Manually inferring and compiling each client’s unique details is also both time consuming and resource-intensive. 

Now, the question is, do you have any technology that can generate these reports and make them as intuitive and personalized as possible? 

The answer is: Natural Language Generation (NLG). NLG is a powerful technology that can convert numerical data into meaningful, easy-to-understand insights. The data that you would need to study for days to create meaningful reports, an NLG-powered platform can use the same data and generate personalized, fact-based, insightful portfolio analysis within seconds. 

Let’s discuss how NLG can be utilized to personalize portfolio analysis reports :

1. Portfolio Snapshot

Much like an overview or executive summary of the report, a portfolio snapshot sums up the most useful and relevant insights into your report, right at the top. Using NLG, you can create a succinct brief of the entire report in a few bullet points which gives your clients a clear understanding of their investment scenario.

Portfolio Analysis Summary

2. Asset Allocation

The asset allocation section depicts how each of the assets are performing and what the client stands to lose or gain if he/she switches to a particular asset with other competitive options. With Natural language generation, you can provide easy-to-grasp actionable statements which not only tell you what has happened but also discloses why it has happened and what needs to be done further with predictive analytics.

3. Segment and Sector Allocation

In this section, the client can see narrative-based recommendations about his potential investments in various segments (large-cap, mid-cap, small-cap). These recommendations help the client understand various aspects of the asset performance in sectors like real estate, stocks, etc., and how investing in either of these will impact the current portfolio. Using NLG to convert the readily available numbers for these sectors takes off the majority of the load off of the portfolio manager.

Asset, Segment & Sector Allocation

4. Detailed Equity Recommendations

In the detailed equity recommendations section, the client can find all the major and minor details related to suggested equities and investment options. Numerical data and graphs only provide a limited understanding. However, using Natural language generation to assess and generate insights and trends about the client’s investment opportunity, can provide meaningful & comprehensive reports without as much labor.

Detailed Equity Recommendations

Interactive Dashboards for Real-time Investment Management

Natural Language Generation has recently become a mainstream technology utilized in reporting automation. Phrazor is one of the most sophisticated platforms catering to a major chunk of the demand right now.

Further, Phrazor has a proven capacity to generate dynamic dashboards that the clients can log in at any time to evaluate and restructure their portfolio without any human assistance, at a click of a button. These dashboards provide filters and switch options to choose from various assets, compare real-time performance of the existing and potential investments, etc. Such NLG powered features corroborate the portfolio advisor’s choices and build lasting trust between the client and the manager.

Simplifying Portfolio Analysis Reports using Automation

Portfolio management involves investing in securities to maximize the investor’s returns and minimize the associated risks. A good portfolio analysis report has multiple objectives, and the end-goal of portfolio management is to ensure the achievement of the said objectives.

Consequently, a portfolio manager’s job is to understand the client’s objectives, the risk levels he is comfortable with, his vision (whether they want long-term or short-term investments), and his expectations from the portfolio.

An integral part of this process is reporting. To earn the client’s trust and to maintain it in the long-term, as a portfolio manager you need to ensure that your portfolio analysis reports have customized data and insights based on an amalgamation of information from returns, allocation summary, and current risks.

Portfolio Analysis: Status Quo

A majority of clients feel that portfolio managers are unable to meet their reporting needs. The reports that clients receive are mostly a bundle of numbers and charts, which make little sense to a client. Clients at times do not understand why structured products and multilayered products are treated differently, the effect of unstable markets on otherwise promising investments. Most investment products have fine details and a complex structure that clients usually do not understand. Moreover, the investment product structures and fine prints change with respect to the market.

On the other hand, the portfolio managers are overburdened by creating reports and maintaining client relationships. Consequently, there is a wide communication gap between the clients and portfolio managers, making life hard for everyone involved.

Further, a client is always at risk of over-diversification and exposure to downside losses. To assure your clients that their investments are well-thought-out and at the minimum possible risk with respect to the market, your most-reliable tool is a well-written and explained report.

Thanks to increasing competition and client expectations, it is vital for an investment firm to ensure that the clients have a working knowledge of how their wealth is being utilized. An increasing number of organizations are using reporting automation software to improve the quality of their reports. Further, reporting automation reduces the manual work required in generating reports and the margin of error significantly.

However, a majority of these software lacks the ability to generate truly customizable reports.

Latest Trends in Portfolio Analysis Reporting for Enterprises

An increasing number of organizations are now depending on BI or reporting tools operating on natural language generation (NLG) technology to extend support for automated report writing. The following are a few trends in the portfolio analysis reporting practiced by leading enterprises in the wealth management industry.

Personalized Portfolio Analysis Reporting
  • Language Capabilities: Reporting automation tools, more significantly the ones using Natural Language Generation and predictive analysis technology, provide support for automated report writing by converting structured data into easy-to-understand, humanized insights. The tools provide the flexibility of generating these insights in any language of your choice to eliminate the barrier of communication. The efficiency of a reporting automation tool highly depends on its easy-to-understandable linguistic abilities along with maximum data accuracy.
  • Customized Insights: An efficient reporting tool allows you to create customized reports for your clients. You can tailor these reports to share personalized recommendations in easy-to-digest narratives that are most relevant to your client as per their portfolio. Further, these insights must be highlighted using custom colors. For example, you can have profit statements marked in green and losses marked in red to make the reports skimmable for the clients.
  • Accessibility Options: These reports are generated in multiple languages and formats (ppt, pdf, audio-visual), which makes it convenient to share them with clients.

Conclusion

Portfolio managers have various visual formats to represent data and make it understandable. However, the inherent message of charts and graphs may be derived differently by each individual depending on their perspective. On the other hand, clear and concise insights backed by data-driven facts, present a wholesome picture of the clients’ portfolio and in turn benefits the portfolio management companies to efficiently cater to the reporting needs of the existing retail investors.

Predictive Analytics: What is it and why it matters!

Most of us have used Amazon for shopping. When we re-open the app and take a look around, we see that most of the screen space is devoted to a “recommended products” section. For instance, if one had been looking for smartphones, they see a section with the trending smartphones that may catch their fancy.

We also see a listed “top categories” section, which may include electronics, health, and personal care, grocery, and gourmet, etc. – based on the categories of products the user mostly shops for. Also, there is a “More items to consider” section that is populated based on the products the user has bought or looked for in the past. How exactly does Amazon make the experience so much relevant and unique to each user? The answer is predictive analytics! Amazon uses algorithms that analyze the user’s shopping trends, search patterns, frequently bought items, etc. to predict their future behavior.

Predictive analytics is a branch of advanced analytics that uses techniques ranging from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about the future. Organizations are looking at predictive analytics to find new opportunities and solve difficult problems.

Improving operations:

Predictive analytics enables organizations to function more efficiently. Many companies use predictive models to forecast inventory and manage resources. Airlines do it all the time to set ticket rates. Hotels try to predict the number of guests per day to ensure that they have enough staff and resources, maximize occupancy and increase revenue.

Detecting fraud:

Organizations combine multiple analytics methods to improve pattern detection and prevent criminal behavior. All the activities on the network are monitored real-time to spot anomalies that indicate fraud or vulnerability, which are then examined for suspicious patterns to make predictions.

Optimizing marketing campaigns:

Predictive analytics helps companies identify customers that are likely to abandon their product or services – they can either be offered incentives to stay or the company can recalibrate its marketing strategy. This also works the other way.

Consider a business that has a $5000 budget for an upsell marketing campaign and has 3 million customers, in which case a 10% discount cannot be extended to everyone.  Predictive analytics and business intelligence can help forecast the customers who have the highest probability of buying the product, then send the coupon to only those people to optimize revenue.

Reducing risks:

Credit scores – a number generated by a predictive model that gives insight into a person’s creditworthiness – are a well-known example of predictive analytics. They are used to assess a buyer’s likelihood of default for purchases and finds application in loan disbursement, insurance claims, and collections, etc.

Advanced artificial intelligence tools backed with predictive analytics like PHRAZOR by vPhrase have taken the market by storm. What makes PHRAZOR stand out is the fact that apart from predicting behavior, it also presents analysis in the form of easy-to-understand narratives which in turn speed up decision making. To prove the point, consider the following snapshot of a client portfolio statement for a leading investment advisory house generated by PHRAZOR.

Personalized Portfolio Analysis Report based on Predictive Analysis

As we see, the report is comprehensible and easily digestible even to a layman. These personalized portfolio reports are provided post thorough analysis of the market, the client’s risk appetite, and investment history. Reports like these lead to happy, satisfied customers – which in turn is increased revenue and brand recognition for the firm! Additionally, PHRAZOR can be configured as required and can seamlessly integrate with the organization’s existing architecture.

To learn more about predictive analytics and how to embed it in your application, request a demo of PHRAZOR.