Data analytics is a crucial tool that today's businesses have at their disposal to gain insights. As artificial intelligence (AI) and machine learning continue to develop and improve, their applications multiply and evolve alongside them. These insights are beginning to shift from the descriptive sort - that is, data that is based on historical information - to predictive analysis - data that looks towards what could happen in the future. There is also another kind of future-oriented analytics - prescriptive analytics - which provides information on how something could happen in the future. And this data analysis is taking place in a variety of new arenas, impacting the way in which we analyze financial information and make business decisions.

The way that we work with tax may change a great deal in the future as a result of progress in analytics, and so could our processes for hiring new employees and monitoring staff performance. As different as tax and human resources may be from each other, both will be impacted by advances in data analytics.

Traditionally, tax data-gathering has focused on hindsight - looking at past transactions for business planning and compliance - but there is an opportunity now with data analytics to consider tax in terms of insight and even foresight.

A study by Deloitte entitled, "Tax data analytics: A new era for tax planning and compliance", says that tax departments face increasing demands to operate more efficiently, while expectations for tax to provide strategic tax viewpoints and additional value to the broader organization continue to grow. In their words:

"Tax data analytics can help address these expanding requirements and open new avenues for tax executives and their teams to engage with the broader business. Tax data analytics combine tax technical knowledge, large sets of data, and new technologies such as visualization tool to generate insights and deeper understanding. Tax analytics can help an organization's tax function make smarter, real-time decisions to improve business performance and drive strategy."

Tax is a field has been hesitate to adopt analytics, but there are signs of growing interest. At a Deloitte Dbriefs webcast, nearly 60% of respondents indicated that their entity was either exploring the use of data analytics or was focused on using it to drive effectiveness and strategy. Most tax software is oriented towards compliance, rather than analytics, and tax law is complex with not a lot of data available to analyze tax structures. Data issues are a great challenge to executing an analytics strategy in tax. However, the demand for strategic tax information and insights is on the up.

The visualization of tax can help equip tax specialists with the ability to explore and explain data in new ways, answering questions about the impact on tax rates of external and internal changes in the business environment.

The last point is something which feeds into Syft's Audit feature - Tax Anomalies, which detects any anomalous transactions that have not had tax applied when they should have. Syft also automatically generates a tax estimate to apply to these anomalous transactions derived from the most common tax rate observed in your account.

Another interesting new arena for analytics is people. People data, also referred to as employee data, is information and insights into the people constituting an organization. In other words, it's a data-driven approach towards Human Resources (HR) Management. This data is collected from systems such as payroll, benefits, Human Resources Information Systems (HRIS), workforce management systems, or talent management systems.

Consistently accurate people data can improve cost management by eliminating hidden expenses and facilitating reliable company-wide insights that can drive business. People analytics uses statistical insights from employee data to make talent management decisions. HR typically collects this data. Recruiting remains the number one area of focus, followed by performance measurement, compensation, workforce planning, and retention.

HR has, historically, gotten a bad rap for being based a great deal on "gut feeling", or on the way things have always been done. This is partially because HR struggles to quantify and measure its success in the same ways in which marketing or finance do. HR analytics changes all of this.

Artificial intelligence (AI) software can analyze video interviews and help assess candidate honesty and personality. There are also tools that analyze hourly labor and immediately identify patterns of overtime and other forms of payroll leakage.

There's been a lot of hype around people analytics, and yet, as Paul Leonardi and Noshir Contractor write for the Harvard Business Review, people analytics understood in terms of data about individual people is very limiting and what should be focused on more is relational analytics (or interaction analytics), data on the interactions between different employees. The raw material for such analytics does indeed exist. You can find it in emails, chats, and file transfers, what Leonardi and Contractor call the "digital exhaust" of a company.

People analytics frequently focuses on unchangeable employee traits, such as ethnicity, gender, and work history; or changeable states, such as age, education level, company tenure, commute distance, number of days absent, and the value of received bonuses.

These are necessary sets of data to consider, but not sufficient in and of themselves.

Relational data captures a snapshot of human social networks and how these impact on work performance. Patterns in this data that correlate with good or bad performance have been likened to the structural signatures in one's brain that can be predictive of various mental disorders, as well as the structural signatures of liquids which chemists look at to predict kinetic fragility.

The adoption of people analytics is not yet widespread, but it is definitely on the rise, which means that the role of HR staff is changing. However, people data is not limited to its use by HR departments. Companies like Ford have expanded their people analytics function across all segments of their business, including finance, HR, and operations.

Looking forward, data analytics is sure to have a large role to play in how organizations run, and this is likely to be bolstered by the increased incorporation of AI. Although tax departments are slower than others on the uptake of data analytics, this is likely to change as more user-friendly technologies emerge that will assist with providing both insights and foresight into tax.

As for people analytics, these are likely to become a major aspect of HR operations moving forward, raising some difficult questions about the role of the human in human resources.

According to Gartner, by the end of 2024, 75% of all enterprises will use AI as part of their operations, leading to a five times increase in streaming data and analytics structures.

"The capabilities of AI are poised to augment analytics activities and enable companies to internalize data-driven decision-making while enabling everyone in the organization to easily deal with data. This means AI helps in democratizing data across the enterprise and saves data analysts, data scientists, engineers, and other data professionals from spending time on repetitive manual processes." - Neerav Parekh, Dataconomy

Managing company data is likely to become more challenging as companies rely more heavily on information as to the validity of incoming or outgoing data. And with an increased reliance on data, interconnectivity will become the key to building a cohesive data analytics system for your business. But as scary as "big data" or AI is often made to sound, these trends offer opportunities to save time and energy, reduce human error, and increase efficiency. They also allow you to refocus your attention on what software cannot do - build interpersonal connections and communicate with emotional intelligence.