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Gut feelings will only drive your business to a certain extent before you’re faced with challenges that demand reasoned and data-driven decision making. As businesses are getting more digitalized, data analytics will play a crucial part in determining the strategic growth of the organization.

If you’re a startup founder, C-level executive, or team leader hoping to spur growth and make wiser business decisions, this article is for you. Read on to unravel the hype of data analytics and how practical implementation can benefit your organization.

The Cost of Poor Decision Making

Companies in a cut-throat competitive environment are faced with numerous critical decisions that must be answered within a short time frame. Slow response, erring when it matters, and plain bad decisions have proved to be costly for many organizations of all sizes.

One of the greatest blunders can be traced to Kodak, for failing to latch on the trend of digital photography, despite being a pioneer in digital technology. Despite its late attempt to leap onto the shifting trend, the gap is too huge for Kodak, and it filed for bankruptcy in 2012.

Faced by thousands of critical decisions, it is inevitable that companies, regardless of sizes, got some of them awfully wrong at times. The cost of these blunders can put a dent into annual profit by at least 3%, according to a publication by Gartner.

A lack of information or data often causes bad decisions. In a world dominated by digital technologies, data-driven decision making is crucial in helping companies to make better choices in areas that matter.

With each decision potentially sending a ripple effect across organizations, it doesn’t make sense to leave such critical processes to chances or gut feelings. Hence, companies should start leveraging data analytics as part of the process.

What is Data Analytics

In the strictest definition of data science, data analytics is a process to inspect datasets and extract information potentially contained within. Patterns and trends are picked up when information, whether historical or real-time, are investigated.

While data scientists are the best people on the job, various software has been built to automate and facilitate the analysis of the data. For professionals, data analytics involves how the data are acquired, stored, controlled, and processed.

As a business owner or non-technical executives, number crunching and trend-spotting may not be your forte. Instead, you’ll want to be more involved with how the numbers are represented in a visual format that can be easily digested when making decisions.

Common Data Analytics Model

Data analytics is a broad term that encapsulates various models employed on the acquired data. It’s essential to understand how each model works as they play different roles in providing different types of insights. Also, executing these analytics depends on the depth of data that a company possesses.

Descriptive Analytics

Descriptive analytics is the simplest form of analysis conducted on a set of historical or real-time data that answers the question of ‘what happened’. For businesses, descriptive analytics is helpful to return quantitative figures of sales, conversions, profits, subscribers, and other figures collected in daily operations.

By using descriptive analytics, stakeholders can quickly grasp an event that occurs, based on the numbers and how they compared to the existing KPIs.

Diagnostic Analytics

Things start getting interesting with diagnostic analytics when data scientists, with the aid of relevant tools, attempt to find out the cause for the events that have been uncovered. In short, diagnostic analytics answers the ‘Why’ of those events that happened.

For example, a dip in sales numbers can be attributed to aggressive campaigns by competitors, or negative press suffered by a company. Diagnostic analytics often have to dig deeper into the data to identify the causal relationship for the present outcome.

Predictive Analytics

“So, what’s next?”. Decision-makers often wonder how the future will play out from the existing situation. Predictive analytics is a methodological approach that utilizes the science of probability to map out potential trends based on past and present data.

While not 100% accurate, predictive analytics enables companies to forecast future trends and make necessary adjustments. Techniques like Monte-Carlo analysis and root-cause analysis are used as a predictive process where multiple influencing factors are taken into consideration. For example, an e-commerce business with adequate data on how users are engaging with its site can predict churn rate and conversion from user engagement.

Prescriptive Analytics

Once you’ve had a clear picture of what’s going on behind the data, you’ll need to take remedial action. Should you remain status quo, or make adjustments for better results?

Prescriptive analytics bears the answer to such questions. By employing various mathematical models, machine learning algorithms, and existing business rules, prescriptive analytics places viable options on the table for key decision-makers.

While prescriptive analytics is by far the most complex, the impact it has on businesses is enormous. Companies are able to determine sales strategy with high accuracy, by delving into past customer behaviors, current trends, and deterministic factors internal and external to the organization.

Risk Management with Data Analytics

Taking risk is part and parcel of being in business. However, there is a fine line between taking calculated risks and heading unprepared into a situation. Smart business owners ensure that each option is adequately evaluated before making any decision.

Data analytics has an important role to play in mitigating business risks. Whether it’s determining core strategies or learning if it’s viable to explore new markets, there’s no better place to look for guidance than the immense data available.

Bankers find themselves benefit significantly from employing data analytics. By tapping into transactional records, customers’ credit performance, and other behavioural data, bankers can make better assessments in mortgage and credit facilities. The chances of customers defaulting on loans are significantly reduced.

Ultimately, the ability to manage risks well in business affects the financial bottom line. According to a Forrester report, companies that are data-driven grow 30% annually and are poised to make $1.8 trillion by 2021.

Businesses that have been suffering from past mistakes will find data analytics a lifeline in navigating the choppy waters. Without accurate data, it’s highly probable that the same mistakes will be repeated.

What’s more important is that businesses can take advantage of the risk alert and move ahead amongst competitors. For example, having a predictive analysis into the lifespan of machinery based on actual usage helps to prevent downtime with contingency plans.

Making Better Decisions with Data Analytics

The benefits of data analytics aren’t limited to the broad scope of risk mitigation but extend to more specific areas of an organization.


Companies have always been bogged down by issues like employee retention, turnover rate, or getting the right person for the job. Instead of browsing through stacks of resumes, or wondering why employees quit months barely into jobs, HR managers can turn to data analytics for answers.

Predictive analytics is the key to solving recruitment woes. HR managers will increase the chances of hiring the ‘ideal’ employee based on the historical performance and resumes of present employees. This allows narrowing the criteria when posting out job advertisements.

High employee turnover rate is a concern for companies, as it saps resources in hiring and re-training new hires. The cost of replacement is as high as 20% of the annual salary for mid-range positions. To stem the outflow, HR managers need to decide on changes to policy, culture, or benefits for the employees.

Rather than grasping in the dark, HR managers can be aided by data analytics before deciding the best course of action. Factors like the current market rate, employee’s tenure in the company, and time-lapse from the last promotion, are deterministic factors of an employer quitting the job.

The same technique can also be used to build a pipeline for talents that are most likely to leave their current positions. Doing so ensures that your company is always covered in terms of talent sourcing.

Customer Engagement

A staggering 84% of customers emphasize customer experience as equally important as products and services, according to SalesForce. Sadly, 54% of customers believe that companies need to do better in customer engagement.

Customer engagement has evolved beyond sending promotional newsletters or popping up a survey form for feedback. Instead, customers are expecting a more personalized experience and, to a certain degree, empathy when interacting with your brand.

In short, you’ll need a more targeted approach when crafting offers or messages to your customers. To do that, you’ll need to dive into the various data that can be acquired from the customer based or third-party sources. Data analytics tools will help in identifying behavioural trends that allow you to devise the appropriate action.

For example, having reliable insights into shopping interests and needs allow e-commerce to recommend relevant products that are helpful to the customers. Even if you’re trying to convert a new lead, having the appropriate data allows the creation of a customized sales funnel that pushes messages that resonate with the prospects.

The applications of data analytics aren’t limited to optimizing digital customer engagement. Brick and mortar businesses can leverage existing customer data and trends to predict shopping behaviours and adapt accordingly.


Companies have minimal tolerance for wastage of time and resources in an increasingly competitive business environment. Productivity becomes an important metric to ensure businesses are getting the most out of their resources.

Often, managers are faced with decisions on policies and processes that could improve productivity or send it plummeting. Take the trend of working from home, for example. Just because it has become a common practice, is it a viable option for your company?

According to FlexJobs, 65% of respondents in a survey found that working remotely is more productive. However, that’s only a single source that leans towards working from home. A data analytics tool will be handy in gathering various inputs, including the internal process of your company, employees’ work routine, and past practices in the same industry before suggesting a work from home policy.

Productivity hack isn’t a one-off process. Even if you’ve reached a conclusion that supports remote working, you’ll need to set up tracking tools to gather data and ensure the arrangement is benefiting your company. Communication between team members, interdepartmental processes, and even menial details like email handling can determine the productivity of your team.

Data analytics also allows you to identify wastages in time and resources in a process. This extends beyond co-working. Companies in the manufacturing sector can leverage data analytics to ensure machines are optimized to minimize waste.

Inventory Management

Managers tasked to oversee the inventory are faced with tough decisions daily. They are assailed with questions of whether to stock up on particular items, and if so, what’s the optimal quantity and price point?

Mishandling inventory can lead to severe repercussions. Best selling items may run out of stock, and companies lose out on revenues. On the other hand, managers may be overzealous in replenishing the stocks, perhaps motivated by the lower price, but results in overstocking.

The delicate balance of good inventory management practice can be aided by data analytics. By canvassing details like seasonal trends, availability, price fluctuations, and shopping behaviour, managers can make better-informed decisions when stocking up.

Rather than sticking to a routine schedule, data analytics enable predictive modelling based on fast-changing variables. Data analytic tools can be integrated into replenishment software that automatically places orders from suppliers based on the existing data.


Marketing is an important arm to ensure the growth of any business. Metrics like conversions and leads are fawned upon by marketers. However, the advancement of data science allows companies to glance beyond these numbers into the persona of the customers.

Data analytics allow marketers to take a different yet more targeted approach into critical decisions in their campaign. Given the fact that most customers interact with a business via different channels, a marketer needs to create a customized campaign for each of the channels.

What works on social media may not be the right approach for paid advertising or SEO. By getting the inputs on how customers interact on different platforms, businesses can have better outreach strategies that are based on proven statistics and on-going trends.

Businesses can also take advantage of data analytics when it scours for new opportunities. For example, expansion in either product or geographics carries a certain amount of risks. Gaining insights into the buying sentiments, local trends, policies, and demographics can aid marketers in making the best calls in execution.

Predictive modelling also helps marketers to determine the probable spending per customer. This can be handy as it allows businesses to tailor the right offer that fits the customer’s budget. What’s equally important is that data analytics can estimate the churn rate of customers and identify the likely contributing causes.

Can You Trust the Data?

While studies are suggesting that data-driven decision leads to better outcomes, the question remains on whether the data are trustworthy? Business advisors do err, and similarly, some numbers may not be showing the real picture.

Therefore, it’s crucial that the data are verified for reliability before they are fed into the analytics. For a start, you’ll need to be aware of the source of the data. Are they credible? Or is there any bias in the data gathering process?

You’ll also want to evaluate the completeness of the data. If you’re mining data from resumes, ensure that information like employment history and educational background are on the records. Claims of the candidate’s skills and certifications need to be traceable and verified.

Often, you’ll need the expertise of a data scientist to aid you in separating ‘good’ data from ‘bad data’. Ensuring accurate data doesn’t stop at the acquisition stage. Even if the numbers have been vetted, they still need to be integrated with existing data.

Depending on the sources, some data may need to be formatted before they can be integrated into existing analytic tools. Failure to do so may result in inaccurate results and negatively influence your decisions.

The Problem isn’t About the Lack of Data…

The best decision is made by considering as many inputs as possible. In today’s highly connected world, decision-makers aren’t troubled by the lack of data but the immense amount of it. The challenge of leveraging data analytics for decision making is to manage the fast-paced and high volume of data flow.

Businesses need to reconsider the approach of how they manage data in their company. Outdated infrastructure may not be able to cope with the rate of updates that are critical to daily operations. Instead, companies need to adopt streaming architecture, which is optimized for storing and managing structured and unstructured data in real-time.

With the ability to react quickly to changing trends and variables, not only business stakeholders can make the right decision, but they are enabled to do so promptly.

Bottom Line

Data analytics has proven to be helpful in making better decisions. More businesses leaping on the bus of data-driven decision making as the cost of ill-informed choices are too hard to bear.

Talk to our team now to learn more about getting the right data tools to support decision making.

Download our whitepaper

Want to know how we have to build a platform based on Apache Kafka, including the learnings? Fill in the form below and we send you our whitepaper.

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