Category: Data Analytics

Data is everywhere. Every click, every transaction, every customer interaction generates a massive amount of information. And while Big Data is often seen as a buzzword, for many businesses, it’s a real challenge—how do you sift through mountains of data and make sense of it all? How do you turn raw numbers into something that can help you make smarter, more informed decisions?

That’s where Business Intelligence (BI) tools come in. With the right business analytics solutions, BI tools can help you transform overwhelming amounts of data into insights that are actionable, valuable, and directly tied to your business goals. Let’s explore how BI tools can help you get the most out of Big Data—and ultimately drive your business forward.

What Exactly is Big Data?

Simply put, it’s the large volume of structured and unstructured data that your business generates every day. This can include customer transactions, social media interactions, sensor data, and even email or chat conversations. In fact, approximately 402.74 million terabytes of data are created each day, according to Statista. That’s a staggering number. And yet, most businesses are still trying to figure out how to process all of this data in a way that makes sense.

Here’s where Big Data management services and business intelligence consulting services can help. They can be the key to organizing, analyzing, and deriving insights from your Big Data, turning what could be a confusing pile of numbers into something you can actually work with.

Why BI Tools Are Essential for Managing Big Data

Managing Big Data manually is a nightmare—trust us, no one has time for that. But BI tools? They’re designed specifically to handle large datasets and provide insights that matter.  You might be wondering, “How do these tools actually work?” Well, think of BI tools as your personal data assistant. They pull together information from multiple sources, clean it up, analyze it, and then visualize it in ways that make it easy to interpret. It’s not just about gathering data—it’s about making it usable.

Here are some of the key ways that BI tools help you make sense of your data:

Data Integration:

BI tools can pull data from different systems (think CRM platforms, social media analytics, etc.) and merge it all into one place. This gives you a 360-degree view of your business, so you can spot trends across all your data.

Data Visualization:

Data on its own can be overwhelming. But when you present that data as charts, graphs, or dashboards, it becomes much easier to understand. You don’t need to be a data scientist to get insights from a BI dashboard—you just need to be able to read the graphs!

Predictive Analytics:

 It’s one thing to look at what happened in the past; it’s another to use that data to predict what will happen in the future. Many BI tools offer predictive analytics, which helps you spot upcoming trends, anticipate problems, and capitalize on new opportunities.

Real-time Analytics:

The business world moves fast, and waiting for weekly or monthly reports can be too slow. BI tools allow you to analyze data in real-time, giving you the ability to make decisions based on up-to-the-minute insights.

The Real Benefits of Using BI Tools

If you’re still on the fence about using BI tools for your Big Data management, here are some of the most compelling reasons to get on board:

Smarter Decision-making:

BI tools give you the power to make decisions based on solid data, not just gut feelings. By analyzing vast amounts of customer data, Amazon fine-tunes everything from product recommendations to inventory management. This data-driven approach isn’t just guesswork—it’s science. In fact, a study from Dresner Advisory Services found that 53% of organizations using BI tools report better decision-making. That’s huge! When you have the right data at your fingertips, you can confidently move forward with your business strategies.

Customer Insights:

Ever wonder what your customers really want? With BI tools, you can dive deep into customer behavior and preferences. This allows you to personalize your marketing, fine-tune your products or services, and create a better experience for your customers. The more you use data management service the better

Cost Savings:

Big Data management services effectively can help you identify inefficiencies within your business, whether it’s in your supply chain, operations, or marketing strategies. BI tools give you the insights to optimize processes and save costs.

Efficiency Boost:

Time is money, and BI tools save you both. By automating data processing and analysis, BI tools free up your team to focus on other tasks that require human insight.Take Netflix, for instance. Their BI systems automate content performance tracking across global markets, freeing teams from sifting through endless spreadsheets. This allows them to focus on acquiring and producing content that aligns with viewer preferences. With BI, you’re no longer wasting time manually analyzing spreadsheets—you can let the tools do the heavy lifting.

Stay Ahead of the Competition:

In a competitive market, knowing what’s happening at any given moment can be a game-changer. BI tools provide you with insights into market trends, customer needs, and competitor strategies. This gives you the edge you need to stay ahead of the curve.

Choosing the Right BI Tool for Your Business

Now that you know why BI tools are so important, you might be asking, “How do I choose the right tool?” With so many BI tools on the market, it can be tricky to know where to start. Here are a few key things to consider:

Scalability:

As your business grows, your data needs will grow too. Make sure the BI tool you choose can scale to accommodate your increasing data volume without sacrificing performance.

User-friendliness:

A powerful tool is only useful if your team can actually use it. Look for BI tools that are easy to navigate, even for non-technical users. After all, the goal is to make data accessible to everyone in your organization.

Integration:

Your BI tool should integrate seamlessly with the systems you already use, such as CRMs, marketing platforms, and ERP systems. The more integrated your tool is, the easier it will be to work with your data.

Customization:

Every business is unique, so you’ll want a BI tool that can be customized to fit your specific needs. Whether it’s creating tailored dashboards or setting up unique reporting, customization is key.

Predictive Capabilities:

As mentioned earlier, predictive analytics is crucial for staying ahead of the game. Look for BI tools that offer strong predictive features to help you plan for the future.

The Power of Business Intelligence Consulting Service

If all of this sounds overwhelming, don’t worry—business intelligence consulting services can help you navigate the process. A BI consultant works with you to understand your business goals, help you choose the right BI tools, and ensure that they’re set up properly.

At BizAcuity, we specialize in helping businesses like yours harness the power of data. Whether you need assistance with business analytics solutions or want expert guidance on implementing BI tools, our team is here to help you every step of the way. 

Final Thoughts

Big Data doesn’t have to be intimidating. With the right business intelligence consulting services and BI tools, you can unlock the full potential of your data and use it to drive better decisions, enhance customer experiences, and stay ahead of the competition.

At BizAcuity, we’re dedicated to helping you make sense of your data with tailored solutions that work for your business. Contact us today to learn more about how we can help you navigate the complexities of Big Data and turn it into a valuable asset.

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Editor’s note: In the article, we outline the reporting capabilities of Dynamics 365 and Power BI. And if you’re looking for more detailed guidance on your CRM analytical options, turn to our Dynamics 365 consultants.

Whether you need to track the number of open opportunities, or spot marketing campaigns that bring the biggest number of leads, Dynamics 365 (as well as its earlier version, Dynamics CRM) has ample analytical capabilities to get the job done. There are Dynamics 365 Reports, Views, Charts, and Dashboards, to name but a few. Still, companies looking to get more insights from their Dynamics 365 data, may find default functionality insufficient for their business goals. And here Power BI analytics comes into play represented by reports and dashboards to give businesses AI-powered answers to their critical questions.

Luckily, Power BI and Dynamics 365 can be integrated quite simply. For example, it takes just a few clicks with Power BI Connector. Still, if you’re not sure whether you need additional reporting capabilities that Power BI offers, check the main differences between Microsoft Dynamics 365 dashboards and Power BI dashboards below.

Power BI dashboards vs. Dynamics 365 dashboards: 3 must-know things

When considering whether Dynamics 365 dashboards or Power BI dashboards would be a better fit for your business, keep in mind the following aspects:

1. Types of visuals

There are 2 types of Microsoft Dynamic 365 dashboards: user dashboards and system dashboards. Employees from sales, customer service and marketing departments can create user dashboards, while system dashboards are created by CRM administrators. User dashboards in Dynamics 365 can include 6 visuals per dashboard maximum and present business data in a variety of ways, like a funnel, a bar graph, a pie graph, and a stack bar graph.

Power BI dashboards, in their turn, can have as many charts (or visuals) as necessary. Among the most popular types of visuals available in Power BI are area charts, bar and column charts, combo charts, funnel charts, doughnut charts, and others.

2. Use cases

Dynamics 365 dashboards are suitable for reflecting day-to-day activities and performance of the sales, marketing, and customer service teams. Typical use cases for Dynamics 365 dashboards are open opportunities on the selected stage or top competitors of the recent period.

Power BI dashboards support more complex and context-based scenarios, showing sales, marketing or customer service trends not only within months and years but in real-time as well. For example, a Power BI dashboard can reflect a social media campaign going viral, or aggregate the sentiment for recent posts and tweets about your product/service. Besides, the tool can predict future trends based on the current datasets.

3. Sharing capability

Dynamics 365 dashboards can be shared with other Dynamics CRM users only. As for Power BI dashboards, they can be shared both with Power BI users and those who have Microsoft Office 365 accounts (free Power BI accounts can be set up within Microsoft Office 365).

Can’t Choose Your Analytical Solution?

ScienceSoft’s team is ready to suggest your best analytics tool with a view to your business needs, company size, and domain.

Which dashboard is better for my organization?

If you’re still in two minds what dashboard to favor, we suggest thinking about the complexity of the data analysis you need. For regular customer analytics tasks not requiring much data slicing-and-dicing, Dynamics 365 reporting capabilities would suffice. And if you need deeper insights entailing examining your customer data from every viewpoint, you’d better leverage Power BI capabilities. If you need help with assessing the feasibility of both options for your business or implementing either of them, you are welcome to contact our team.

In today’s data-rich environment, the challenge isn’t just collecting data but transforming it into actionable insights that drive strategic decisions. For organizations, this means adopting a data-driven approach—one that replaces gut instinct with factual evidence and predictive insights. BI consulting services play a central role in this shift, equipping businesses with the frameworks and tools to extract true value from their data. As businesses increasingly rely on data for competitive advantage, understanding how business intelligence consulting services foster data-driven decisions is essential for sustainable growth.

What is BI Consulting?

Business intelligence consulting services offer expertise and guidance to help organizations harness data effectively. Beyond mere data collection, BI consulting helps businesses create a cohesive data strategy that aligns with organizational goals. This approach involves everything from identifying key metrics to implementing analytics systems and designing dashboards. A skilled business intelligence consultant helps organizations turn raw data into insights, providing a foundation for smarter, more informed decision-making.

The Significance of Data-Driven Decision-Making

In sectors ranging from healthcare to finance, data-driven decision-making has become a strategic asset. Making decisions based on data, rather than intuition alone, brings benefits such as increased accuracy, reduced risks, and deeper customer insights. Data-driven organizations report greater efficiency and better customer satisfaction as they can act on real-time insights rather than retrospective analysis. BI consulting services empower companies to unlock these advantages, providing not only the technical setup but also the strategic guidance needed to transform data into a powerful decision-making tool.

Challenges in Achieving Data-Driven Decision-Making

While the benefits are clear, many organizations struggle to become fully data-driven. Challenges such as data silos, inconsistent data quality, and a lack of skilled personnel can create significant barriers. These issues often lead to fragmented information and missed opportunities, as departments operate on isolated data streams. BI consulting services address these pain points, helping organizations establish centralized data management practices, ensure data consistency, and implement solutions that break down these silos. 

How BI Consulting Fosters Data-Driven Success

Data Strategy and Business Alignment

One of the core roles of business intelligence consultants is aligning data initiatives with business objectives. This strategic approach enables organizations to prioritize data projects that support their key goals, whether they aim to improve customer experience, reduce costs, or expand into new markets. By aligning the data strategy with business needs, companies can focus their resources on initiatives that yield the most value.

Data Integration and Quality Management

Fragmented data across various systems is a common challenge that hinders data reliability. Business intelligence consulting services often focus on integrating data from different sources, ensuring that organizations have access to a single, trustworthy version of the truth. By consolidating and cleaning data, BI consultants help businesses access high-quality information that informs decisions effectively and accurately.

Advanced Analytics and Predictive Insights

The real value of data lies in its ability to forecast trends and identify opportunities. Advanced analytics and predictive modeling are core offerings of BI consulting services, enabling organizations to move from descriptive reporting to proactive decision-making. Through these insights, businesses can anticipate customer needs, optimize supply chains, and prepare for market changes before they happen.

Data Visualization and Dashboard Creation

Complex datasets require intuitive visualization tools to make insights accessible. BI consultants often design dashboards and reports that simplify data interpretation, empowering leaders to identify patterns and trends quickly. Data visualization enables different teams across an organization to access relevant insights in real-time, making it easier to act on data and implement changes promptly.

Benefits of BI Consulting for Modern Organizations

Engaging business intelligence consulting services brings a range of benefits, enabling companies to not only make better decisions but also respond to market demands more effectively. Key advantages include:

• Improved Decision-Making Agility: Data-driven insights allow companies to react to industry changes, customer behaviors, or operational needs in a timely manner, giving them a competitive edge.
• Enhanced Efficiency and Reduced Redundancies: BI consultants help streamline operations by identifying and eliminating inefficiencies, which can result in significant time and cost savings.
• Increased Customer Understanding: Analytics helps companies identify trends and preferences, allowing them to tailor products and services to meet specific customer needs.
• Greater Profitability Through Informed Resource Allocation: With data insights, businesses can allocate resources more strategically, leading to cost reductions and revenue growth.

Key Considerations When Choosing a BI Consulting Partner

Choosing the right BI consulting partner can make or break a data strategy. Organizations should look for business intelligence consultants with deep industry expertise, strong technical capabilities, and a proven ability to understand business-specific challenges. A good consulting partner not only provides technical solutions but also collaborates closely with internal teams to ensure that every aspect of the data strategy aligns with the organization’s vision and objectives.

Conclusion

The potential of data to drive smarter, more strategic decisions is undeniable. Yet, achieving this potential requires a structured approach, the right expertise, and an unwavering commitment to using data responsibly and effectively. BI consulting services play a pivotal role in helping organizations harness their data to foster a culture of data-driven decision-making. By building a foundation of reliable data and advanced analytics, organizations can confidently leverage insights to stay competitive, innovate, and grow in an increasingly data-dependent world.

For businesses striving to elevate their decision-making, BI consulting is more than a service—it’s a strategic investment in the future of informed, data-led success.

It’s true that technologies and machines made it easier to work safely and successfully complete tasks. Yet, people are still responsible for making important decisions on their own. A surgeon can perform the most intricate operation with the help of powerful tech tools, but technological capabilities don’t really matter if the initial diagnosis was incorrect. In this case, the wrong decision may cost as much as a patient’s life.

Judgement of those who are to estimate and consult may ‘drift’ – that is, deviate from precise calculations or correct decisions. Such drift can have grave consequences for businesses: over the years, companies insensibly lose billions due to inaccurate decisions of their specialists, as confirmed by Daniel Kahneman’s research group. According to his study, even if the difference between two employees’ case assessments of $1,000 and $600 respectively is only in $400, the same 40% judgment variation will be present in decisions involving much larger costs.

Understanding the nature and reasons of judgment drift can help to come up with a tool that will control it and minimize its effect to the largest extent possible.

What causes drift in decision-making

All professional decision-making tasks need expert-level knowledge. However, some decisions have very strict and precise regulations, while others leave space for personal judgment. The latter open a possibility for noise – influences of personal circumstances (mood or the physical state), stereotypes, or personal beliefs and principles that at times aren’t even acknowledged.

For instance, although the aim of both a loan provider and a recruiter is to assess a person they deal with, they are in quite different situations. The loan provider analyzes the financial capability of a future customer (with 80% of their decision based on figures and formulas), while the recruiter uses information from CVs, tests and interviews (their decision is about 60% based on personal impression). As a result, the recruiter’s less regulated choice is more likely to be inaccurate.

Forecasting disease progression, predicting purchase rates and estimating the cost of possessions or damage – all these judgments are prone to noise and bias. The more they can be guided and regulated, the more accurate a decision will be.

Algorithms help to control noise and bias

According to the studies highlighted in HBR’s article of October 2016, algorithms are especially effective on the final stages of decision-making. As mentioned in the paper, after US judges started using a consulting algorithm that helped them determine whether a defendant was safe to be released until trial, the crime among defendants decreased by 15%. Meanwhile, the amount of people released before trial skyrocketed, which vacated detention centers.

Contrary to the common misconception, algorithms don’t have to use complex databases nor do they completely replace people at work. Most algorithms can simply ask people to input data after analyzing the situation and narrowing it down to a number of facts. This way, the final result is based on the professional knowledge but is deprived of bias.

Algorithms can be shaped as a desktop or mobile app, and the latter is, by all means, a more flexible and convenient solution. With the help of an IT consultant, companies can also understand whether they will benefit more from standalone mobile app development or from implementing decision-making algorithms as a part of a complex enterprise mobile solution.

Why and how a mobile app can help

Judgment is not tied down to a desktop-equipped workplace. Some decisions involve collecting information in the field (recruiters on interviews, estimators on-site), some require an instant answer to a customer’s question (shop consultants and salespeople in the field). Enabling a decision-making tool for mobile isn’t just a matter of convenience but rather of common sense and expediency.

Electronic checklists can be useful for estimators of antiques, real estate, car damage or literally anything else. An app will display lists of descriptive characteristics and specifics, different and customizable for any object. By paying attention to all the necessary details mentioned in the app, an expert will later base their decision on as much information about the object of interest as possible.

Once the necessary data is collected, it can be put in the app’s calculator. The method of input varies on the initial data type: a consulting expert can, for instance, enter exact parameters and get a final price, while a healthcare expert will see scales where they should rate their patient’s condition to get a type and length of treatment.

A mobile app can offer a basic eligibility test to learn if a customer or a candidate matches a company’s offering. By answering simple ’yes/no’ or scale-based questions on their mobile devices, recruiters will quickly find out if they should proceed to other interview stages with a certain candidate, and an insurance company will know if they should accept the application. Eligibility tests can also include special conditions to show, for example, which special offers fit a particular customer.

Some decisions aren’t made by one person but are discussed collectively. However, it is often the case that the final decision is the one that is proposed first or announced in the most confident tone, while other good ideas stay in the background.

To make sure all committee members have their say, a company can introduce a feature that will ask to contribute ideas prior to roundtable meetings. On the meeting itself, all the opinions offered will be open for discussion.

Endnote

Since circumstances and stereotypes constantly influence people, judgment drift is frequent in business decision-making. Yet, technology consulting and mobile app development can pave the way to effectively minimizing it. Checklists, calculators, eligibility tests and opinion surveys in a mobile app can ensure that decision-making of field or office-based experts is accurate and doesn’t get impeded by noise and bias.

From our 8-year experience in providing big data services, dashboard design seems to be the most underestimated. Unfortunately, not all the companies share the thinking of Rolls-Royce that believes visualizing big data is as important as manipulating it. Most often, companies don’t realize how much these fancy graphs and charts contribute to making informed decisions until they have to find some valuable insights within seconds among the millions of data records.

Big data visualization makes a difference

John Tukey, a celebrated mathematician and researcher, once said: “The greatest value of a picture is when it forces us to notice what we never expected to see.” And our data visualization team couldn’t agree more. Visualization allows business users to look beyond individual data records and easily identify dependencies and correlations hidden inside large data sets.

Here go examples of how big data analysis results can look with and without well-implemented data visualization.

Example 1: Analysis of industrial data

In some cases, the maintenance team can skip the ‘looking for insights’ part and just get notified by the analytical system that part 23 at machine 245 is likely to break down.

Nevertheless, the maintenance team is unlikely to be satisfied with instant alerts only. They should be proactive, not just reactive in their work, and for that, they need to know dependencies and trends. Big data visualization helps them get the required insights. For example, if the maintenance team would like to understand the connections between machinery failures and certain events that trigger them, they should look at connectivity charts for insights.

Example 2: Analysis of social comments

Imagine a retailer operating nationwide. One customer may visit their store and post on Facebook: “Guys, if you haven’t bought Christmas presents yet, go to [the retailer’s name].” Another customer may share on Twitter: “I hate New Year time! I’ve never seen lines that long! I wasted an hour at [the retailer’s name] today. And the staff was rude. Hate this place!” The third customer may post on Instagram: “Look what a gorgeous reindeer sweater I bought at [the retailer’s name]!”

The company’s customer base is 20+ million. It would be impossible for the retailer to browse all over the internet in the search of all the comments and reviews and try to get insights just by scrolling through and reading all the comments. To have these tasks automated, companies resort to sentiment analysis. And to get instant insights into the analysis results, they apply big data visualization. For example, word clouds demonstrate the frequency of the words used. The higher the frequency, the bigger a word’s font. So, if the biggest words are hate, awful, terrible, failed, and their likes – it’s high time to react.

Example 3: Analysis of customer behavior

Companies use a similar scenario to analyze customer behavior. They strive to implement big data solutions that would allow gathering detailed data about the purchases in brick-and-mortar and online stores, browsing history and engagement, GPS data and data from the customer mobile app, calls to the support center and more. Registering billions of events daily, a company is unable to identify the trends in customer behavior if they have just multiple records at their disposal. With big data visualization, ecommerce retailers, for instance, can easily notice the change in demand for a particular product based on the page views. They can also understand the peak times when visitors make most of their purchases, as well as look at the share of coupon redemption, etc.

Most frequently used big data visualization techniques

Earlier, we studied on practical examples how companies can benefit from big data visualization, and now we’ll give an overview of the most widely used data visualization techniques.

Symbol maps

The symbols on such maps differ in size, which makes them easy to compare. Imagine a US manufacturer who has launched a new brand recently. The manufacturer is interested to know which regions liked the brand particularly. To achieve this, they can use a map with symbols representing the number of customers who liked the product (left a positive comment in social media, rated a new product high in a customer survey, etc.)

Line charts

Line charts allow looking at the behavior of one or several variables over time and identifying the trends. In traditional BI, line charts can show sales, profit and revenue development for the last 12 months. When working with big data, companies can use this visualization technique to track total application clicks by weeks, the average number of complaints to the call center by months, etc.

Pie charts

Pie charts show the components of the whole. Companies that work with both traditional and big data may use this technique to look at customer segments or market shares. The difference lies in the sources from which these companies take raw data for the analysis.

Bar charts

Bar charts allow comparing the values of different variables. In traditional BI, companies can analyze their sales by category, the costs of marketing promotions by channels, etc. When analyzing big data, companies can look at the visitors’ engagement with their website’s multiple pages, the most frequent pre-failure cases on the shop floor and more.

Heat maps

Heat maps use colors to represent data. A user may encounter a heat map in Excel that highlights sales in the best performing store with green and in the worst performing – with red. If a retailer is interested to know the most frequently visited aisles in the store, they will also use a heat map of their sales floor. In this case, the retailer will analyze big data, such as the data from a video surveillance system.

How to avoid mistakes related to big data visualization?

The main purpose of big data visualization is to provide business users with insights. Choosing the right visualization tool among the variety of options on the market (Microsoft Power BI, Tableau, QlikView, and Sisense are just a couple of product names) and applying the right techniques to create uncluttered and intuitive dashboards may appear to be a more complicated task than it seems. If you feel that you need any assistance with this issue, you can involve big data consultants to help you choose the most suitable visualization solution and/or customize it.

During my 16 years of BI implementation and consulting practice, I got used to treating the term ‘best practices’ with the utmost care, as quite often it’s just a fine word intended to capture the audience’s attention. Here, I decided to share my approach to distinguishing seeming business intelligence best practices from genuine ones with those readers who are about to start their BI implementation projects.

BI best practices that aren’t really helpful

I won’t recommend dedicating too much time to business intelligence best practices that simply share general organizational tips applicable to any IT solution. If you spend more than 5 minutes and get as much as ‘define the strategy first’, ‘get organization-wide buy-in’ or ‘don’t build everything on day 1’, you’d better switch to another source of wisdom and get really useful information.

Best practices may be BI-specific, still superficial. For example, I doubt that businesses can get much value from ‘identify preferred data integration method’. This advice leaves too many questions, some of which are ‘What integration methods are available in general?’, ‘Should we try them all in practice or just learn in theory?’, ‘Whose preferences should we rank the highest?’ Another example of a similar best practice is ‘Implement an easy-to-use BI application that integrates with desktop applications’.

Where to get valuable BI best practices?

Check industry leaders 

For helpful insights, you can check what BI initiatives the industry leaders have chosen and implemented. Say, if you are a retailer, you can see that major players in your industry focused on such initiatives as 360-degree customer view, customer churn prevention, and demand forecasting. If you are a manufacturer, you are most likely to find out about predictive maintenance and supply chain optimization. Currently, such information is widely available – you may find many articles describing real-life use cases with a decent degree of detail, quite often even with the numeric effect on revenue or customer satisfaction.

Study BI initiatives for small and midsized business

It is useful to explore BI initiatives implemented by midsized and small businesses (especially, if your company belongs to any of these segments) to find segment-specific use cases and examine sample business reports and dashboards. To get this information, you can turn to vendors’ case studies, like the one from our project portfolio – BI implementation for a management consultancy.

Explore BI trends

Also, it makes sense to read about the recent BI advancements and trends. For such insights, you can explore comprehensive whitepapers by research and advisory companies like Gartner, TDWI, McKinsey & Company, BARC. For example, according to BARC that voices the opinion of 2,600+ respondents, the list of BI trends that are important in 2019 includes such aspects as data quality management, data visualization, self-service BI, data warehouse modernization, agile BI development, and advanced analytics. You can also turn to reputable media platforms, such as Forbes.com and CIO.com, that also often post articles with business intelligence or data analytics trends.

To sum it up

If you search for valuable information, check the initiatives of industry leaders, use cases specific to a certain company segment, as well as recent BI advancements and trends. They will help you to better define your BI needs and put them into practice.

Editor’s note: Irene provides a comprehensive overview of data analytics outsourcing, the advantages it brings, and the concerns it raises. If you consider outsourcing your data analytics solution, feel free to explore our offering in managed analytics services.

Own-or-rent decisions are well applicable to the analytics domain: businesses need to choose whether to opt for building their in-house data analytics solutions or outsource the service. As the global market for the latter is expected to grow annually by 22%, I suggest you have a closer look at data analytics outsourcing.

Data analytics outsourcing at a glance

Data analytics outsourcing is the cooperation model under which a company entrusts a service provider with its data and gets access to insightful reporting. At the same time, the provider takes care of everything else: infrastructure setup and support, data management and data analysis.

Two examples to envisage the service

Imagine an FMCG manufacturer suffering from unstable sales performance but unable to either run diagnostic analytics to identify what triggers poor performance or make accurate sales forecasts. At the moment, the manufacturer has neither time nor budget to grow their in-house data analytics team, so they turn to analytics outsourcing, hoping for fast insights.

To start delivering the service, the vendor needs the manufacturer’s data. They clean and organize it, and in the 4-week time, they provide the manufacturer with access to the Power BI reporting tool with comprehensive analysis revealing the reasons for sales instability and the potential to increase the manufacturer’s sales by 15%. The vendor also provides the sales forecasts powered with data science.

Image credit: Microsoft

Another example demonstrates big data outsourcing. Think of an online entertainment provider having hundreds of thousands of visitors daily who search for the songs, the singers and the playlists they like. Meanwhile, the provider collects tons of data about their visitors – the day and time they visited, searches they made, singers they listened to, songs they liked and disliked, playlists they created, and a plethora of other data.

The provider runs only a basic analysis of this data. For example, they calculate daily traffic, define the busiest time when the majority of users turn to the service, identify the most popular songs and singers. However, the provider finds these insights insufficient for their business needs – they want to have their customers segmented, understand the preferences of each segment, stay updated with their behavior and personalize the service as much as possible. To achieve that, the provider’s current big data solution should be enhanced with big data analytics capabilities. As the solution reworking will require months and significant investments, the provider decides to outsource their big data, pay a subscription fee, and start to get the required insights within a couple of weeks.

Do you need such actionable insights right now?

ScienceSoft can help you skip the technicalities and satisfy your data analytics needs within time and budget on a subscription fee basis.

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Service timeline and the costs

Let me make it clear, to benefit from fast insights with data analytics outsourcing, you should be ready for long-term cooperation (typically, 2+ years) and a monthly subscription fee. A subscription fee covers data preparation and management activities, as well as the agreed number of regular and ad hoc reports.

And what concerns an alternative approach to data analytics, an in-house solution would require the costs of the solution’s design, implementation and support, the costs of hardware or cloud subscriptions and software licenses, and the costs of keeping an in-house analytical team.

The reasons to opt for data analytics outsourcing

A ready-to-run service

Contract signing and service deployment by a vendor, which usually takes from 6 to 8 weeks, is the only thing that keeps you from getting access to the batch of agreed reports and getting the value out of your data. For comparison, the design and implementation of an in-house BI solution that would enable the same reporting can take from 6 to 8 months.

Industry-specific best practices

A professional outsourcing partner elicits your requirements for reporting and brings in industry-specific best practices. For example, at ScienceSoft, when working with clients, we provide detailed consultations on what kind of reports will bring more insights, what data sources should be used to create such insightful reports and much more.

Technology and process expertise

Your outsourcing partner saves you the trouble of exploring, say, the differences between Apache Cassandra and HDFS (two technologies that can be used to build a big data storage) by taking complete care of the technology side. The vendor decides what technologies to choose to process the data you have and with the performance you require. They have hands-on experience of implementing, integrating and managing different business intelligence and big data technologies.

For example, one of ScienceSoft’s clients had clear requirements regarding the analysis but wanted us to take the responsibility for its technical embodiment. Thus, a US management consultancy could get pre-built reports with valuable insights about their particular aspect of interest skipping all the technicalities.

The aspects of data analytics outsourcing that usually raise concerns

Here, let me address the questions that companies most commonly ask before opting for outsourced analysis.

How to choose the right outsourcing partner?

The first thing I suggest doing is to check the vendor’s expertise. For this purpose, years of experience in data analytics, as well as the portfolio of implemented projects will serve the best. While studying the portfolio, pay special attention to the projects that belong to your industry – it’s the simplest way to understand whether the vendor will be able to bring valuable insights to your business. It will also be wise to check the vendor’s partnerships and certificates.

Another important aspect is the quality of collaboration – check such basic things as speaking the same language (literally) and the ability to communicate within your working hours, as well as more advanced things like friendly and proactive approach and effective conflict resolution.

Will our data be secure?

Many of the companies, who commission ScienceSoft as a data outsourcing partner, feel uneasy about sharing their data, which is absolutely normal. Being fully aware of the importance of this issue, we ensure the same or even higher level of data security than when the data is stored internally. So, my advice to you is to cover the aspect of data security in the outsourcing contract: clearly describe the requirements to the environment where the vendor should store your data, as well as security measures and the liability imposed on the vendor with regard to ensuring data security.

Should we be actively involved in the process?

Normally, a company’s active involvement is required at the discovery stage, when the outsourcing partner scans the as-is situation to understand the analytics needs of the business better. Based on the findings of the discovery stage, the partner prepares a service-level agreement (SLA) and agrees upon its terms with you.

A good outsourcing vendor self-manages their work due to mature and transparent processes. They take over the full responsibility for the service delivery and its quality. However, I recommend that you establish continuous communication with the vendor, for example, provide them with timely feedback on the new features or evaluate the vendor based on the criteria defined in the SLA.

Will we get the description of the analytical models used?

The vendor doesn’t have to share with their customers what analytical models they tried and which ones were recognized the best. The same applies to the architecture of the data analytics solution or a deep neural network, the hyperparameters and configurations. In a word, the vendor has the right to leave all the technicalities behind the scenes (unless otherwise specified in the agreement).

Tips to make your outsourcing contracts effective

Below, I’ll briefly describe the essential aspects that should be covered in a contract with your outsourcing vendor.

Key performance indicators and service level objectives

With wisely chosen KPIs and well-defined SLOs (service level objectives used to evaluate the vendor’s performance), you can clearly express your objectives and set the direction for your outsourcing partner, as well as exercise control over the service rendered. In their turn, the outsourcing partner knows what you expect from them, and they can organize their work and allocate their resources in the way they find most effective for reaching the objectives.

It’s important to provide each SLO with a thorough description and a measurement interval. Have a look at some examples of KPIs and SLOs:

Reports and communication

In the SLA, specify how frequently the outsourcing partner should provide you with a report on their work. I also advise you to particularize how often your communication with the outsourcing partner should take place, what project roles on your and the partner’s side should communicate directly, and what should be the aspects of their collaboration. For example, the users in your business departments can address the vendor’s analysts directly to provide their feedback.

The opportunity to cancel the contract for non-compliance

Make sure that your outsourcing contract contains a clause stating that your company can terminate the contract if the partner fails to meet the deadlines or other SLOs. There’s another reason for paying due attention to the detailed descriptions of SLOs, as well as setting forth the agreements on reports and communication.

So, does your business need data analytics outsourcing?

To answer that question, have a look at some strong indicators defined by ScienceSoft’s data analytics team:

• You want to get value out of your data fast (within weeks, not months).

• You don’t have time or resources to develop and support an in-house solution.
• Data analytics competence at your company is yet to be developed.

If you have found yourself in any or all of the above situations, my advice to you is to consider data analytics outsourcing, so feel free to contact me for further details.

At ScienceSoft, we consider operational BI as an evolutionary step of traditional BI. Traditionally, business intelligence was available to professional data analysts and top managers only, allowed producing monthly and weekly reports and served as grounds for strategic and tactical decisions.

However, gradually organizations realized the benefit of involving more employees in data-driven decision-making and having BI to support operational decisions. Thus, the era of operational business intelligence began several years ago. Let’s check whether the concept is still on the radars of companies looking for BI implementation.

Advantages that operational BI brings

We often see two main advantages that operational BI brings:

Providing real-time insights

Operational BI enables business users to quickly spot an emerged opportunity or a problem and react accordingly. This can be achieved with operational reports and dashboards (where the information is updated within a set time interval – say, every 10 minutes or every hour), as well as triggered alerts or messages. For example, line supervisors can get hourly production reports that will help them to reach their daily production targets.

Fostering a data-driven corporate culture

Operational BI is also unofficially called ‘BI for the masses’, as it enables the majority of business users – from the senior managers to frontline workers, such as call center agents and sales representatives, to get access to up-to-date data they need in their work. When employees are trained to turn to a robust BI tool for insights, they get used to being guided by data in their decisions and activities.

Watch BI in action!

ScienceSoft shows how a couple of customized dashboards can tell you the whole story about your company’s health and performance.

Challenges of implementing operational BI

And here are the typical challenges that we solve while implementing operational BI projects:

Ambiguous ‘real-time’

The term ‘real-time’ covers a wide time range – from milliseconds to hours, so a company’s departments should be as explicit as possible about their expectations of real time. Otherwise, the odds are high that an implemented BI solution won’t satisfy the needs of all the business users.

Special requirements at the design and implementation stages

Due to the real-time nature of operational BI, a BI implementation team should draw special attention to data cleaning and validation rules, as they need to find a trade-off between data quality and the speed of data processing. Besides, they should come up with the architecture and configuration that would enable the solution’s fast performance.

The state of operational BI 

We perused data analytics and business intelligence trends and found the following:

• G2’s Learning Hub predicts the rise of real-time analytics.
• 2,600+ respondents of BARC’s survey stated BI with real-time data among 20 most important BI trends.

Despite neither of the trends mention operational BI explicitly, they both relate to it as real-time insights are one of the main advantages of operational BI.

These predictions are perfectly in line with what we see in our BI consulting and implementation practice. Our latest engagements show that operational BI is already on the radars of enterprises with 1,000 – 10,000 employees, which often need to promptly analyze big data in addition to traditional data. Thus, traditional and operational BI naturally go hand in hand, as, besides instant insights and alerts, companies need some thorough analytics based on historical data, for example, to diagnose root causes of identified problems.

To sum it up

Businesses find operational BI very important, mainly due to its ability to deliver real-time insights. At the same time, we see the convergence of traditional and operational BI, as well as the growing importance of collecting and processing big data.

Alex Bekker, our Head of Data Analytics Department, depicts ScienceSoft’s proprietary BI framework that we formed during 16 years of our business intelligence practice.

Companies don’t need business intelligence (BI) just for the sake of it. A company’s objective is informed decision-making, and a BI solution should enable it. One thing disregarded during BI implementation (like proper business performance monitoring or getting timely info about business operations), and the company may feel frustrated with the deliverables.

Business intelligence framework

To ensure that our customers get the maximum benefit out of their BI implementation projects, we have developed a BI framework that covers all the functions that a BI solution should perform. Below we illustrate the framework’s four main components by the example of YourStyle, an imaginary fashion manufacturer and retailer. For your convenience, we accompany the descriptions with visuals.

Planning

The planning component allows identifying trends, creating forecasts, measuring business performance and analyzing plans.

• With trend analysis, BI users are able to identify patterns in historical data and understand the opportunities that a company can catch, as well as challenges that they have to overcome. For example, YourStyle’s executives can track how monthly gross profit has developed during the year.

• Forecasting allows predicting a future trend to set goals. YourStyle set their gross profit targets after their gross data analysis for the last 5 years. As we show YourStyle’s dashboards designed for executives, we can’t see the forecasting, which falls under the responsibility of data analysts.
• Performance analysis encompasses internal and external benchmarking. YourStyle can find top performers internally, say, by analyzing which of their collections brought the highest sales and gross profit. However, the company can get more insights by integrating the data about the typical and high performance in the industry.

• Plan analysis. YourStyle has their plans in a BI solution. They don’t have any mismatch among the plans when they look at them from different perspectives like gross profit by state, by month, and by collection. As both dashboards we examined are tailored to YourStyle’s executives, we see only top-level plans there. However, be assured that these plans can be further broken down to the plans of different departments.

Plan execution

The essence of the plan execution component is to compare the planned performance against the actual one. For instance, YourStyle’s executives can easily spot that the company has reached their net profit target by 66% only.

Besides, YourStyle’s BI solution allows undertaking root cause analysis. You can take a guided tour through a dedicated BI demo to see how YourStyle identifies the reason for lower-than-planned profit performance.

Live BI demo

Change analysis

Change analysis allows identifying real or potential consequences of a change. YourStyle doesn’t have this component implemented yet. However, if they had, they could run different scenarios to understand what impact the introduction of a new collection might have had on their business.

Optimization

The essence of the optimization component is to use the data from a BI solution to optimize internal business processes. YourStyle can employ the insights they get to improve their marketing by reconsidering the advertising mix for launching a new collection and sales by bringing best practices to the states that are underperforming.

Are you exploiting the full potential of your business intelligence?

Now, you have a checklist to answer this question with confidence. If you have planning, plan execution, change analysis, and optimization components covered, well done – you should already benefit from informed decision-making; if not yet – you have a reference BI framework to identify the missing components and prioritize where to start.

Editor’s note: In this article, Irene leverages ScienceSoft’s experience to share three approaches to Business Intelligence (BI) as a service from the perspective of analyzed data. If you want to learn more about the value BI as a service can bring, explore our offer in managed analytics services.

‘Owning’ a business intelligence (BI) solution is no longer the only possibility for a company striving for informed decision-making. The data analytics outsourcing market is forecasted to grow, which means that more and more companies will choose ‘renting’ their BI over any type of implementing it.

Before taking a closer look at the approaches to BI as a service, let me quickly define the main differences between BI as a service (also known as managed analytics services or data analytics outsourcing) and BI implementation services:

• BI implementation presupposes a 6-8 month project. Once it’s completed, a company gets a data analytics solution (on-premises, in-cloud, or hybrid one).
• BI as a service presupposes continuous cooperation, where the outsourcing partner is totally responsible for the solution’s technical embodiment, and the company gets access to first analytical insights in 1-5 days from the cooperation start.

BI as a service based on internal data

Relying solely on the data retrieved from a company’s systems, this approach provides insights into the company’s business processes, customers, performance, and more. As a main advantage of this approach, I would mention a plethora of data available for the analysis.

For illustration, let me draw your attention to one of ScienceSoft’s projects, where a metal parts manufacturer requested us to help them prioritize their product categories as they wanted this strategic decision to be data-driven. In the course of the project, ScienceSoft scrutinized the manufacturer’s 2-year financial and production history data into meaningful insights and built reports and graphs so that they could glance at the findings and find the answers to their business questions.

However, there is a significant drawback in getting insights derived from internal data solely. The company is limited to their ‘internal wisdom’ and lacks external data for sound comparison.

Be one of those companies that make quick and fact-based decisions!

ScienceSoft can help you derive actionable insights out of your company’s data with minimal efforts spent on your side.

BI as a service based on external data

This approach is frequently used to run strategic market analysis or social media analytics. The biggest challenge while working with external data is ensuring data quality, as it usually comes from multiple disintegrated data sources and can contain errors and discrepancies.

Have a look, how this approach was realized in our other project, where a management consultancy outsourced the analysis of market data to ScienceSoft to get valuable insights into different industries in various economic conditions. To solve this task, we deployed the BI infrastructure with a data warehouse and an OLAP cube of 40+ dimensions on our server, and our customer was provided with the access to the pre-built reports and dashboards, with the possibility to run ad hoc analysis.

Hybrid approach

Using data from both inside and outside, a company can get the broadest picture that encompasses both their internal operations and the market perspective. No wonder that a hybrid approach is the most widespread.

According to the Business Application Research Center (BARC), companies mostly derive data from multiple data sources. Besides, there’s a trend towards increasing the number of data sources (a half of BARC’s respondents believe that they already experience this trend). Also, the number of data sources naturally depends on the company size: BARC names a median number of 5 internal sources for mid-sized companies and 10 sources for large ones.

Is there the best approach?

Of all the approaches, I consider the hybrid one the most efficient as it ensures the variety of data sources and offers better scope for analysis. However, as you may see from the examples I’ve outlined, the scenarios with getting insights from just internal or external data are also possible. So, if you need to define which approach will work better for your business, let me know.