Category: Data Analytics

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.

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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.

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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.

A business has two options if they want to grow their data analytics capabilities: developing an in-house data analytics team or making use of managed analytics services. Multiple companies prefer the traditional first option, believing that growing an in-house team is a safe choice. Undoubtedly, this strategy can be powerful, still it’s associated with certain challenges. Having looked back at my practical experience, I’ve outlined a typical profile of a company growing their in-house analytics team and picked out 3 challenges such companies are likely to face in the process.

A typical profile of a company that is to grow their in-house analytics team

Meet Prod&Sell, a large manufacturer and retailer, which is currently present in 15 states. Although the company has a data analytics team, they lag behind Prod&Sell’s evolving analytics needs. While Prod&Sell’s C-suite and business units expect reliable and timely reporting, self-service and predictive analytics (to name a few things), the analytics team still collects data from disintegrated sources, cleans it and prepares reports manually.

To satisfy the company’s analytical needs, the C-suite opts for creating a business intelligence solution with embedded big data analytics and data science capabilities. Colin, the Head of Prod&Sell’s data analytics, is charged with the task to further develop his department to implement this ambitious project.

Challenges of developing an in-house data analytics team

Here are the challenges that are ahead of Prod&Sell and Colin:

Lack of required talent

First, Colin has to build up a team with mastery in many domains:

• BI and big data: to design and implement a data lake, a data warehouse, OLAP cubes, reports and dashboards, as well as administer the implemented solution on a daily basis.
• Data science: to design machine learning models and tune their hyperparameters, train and retrain them, and deal with noise reduction.
• Data quality and data security: to set up and automate a data quality management process and ensure role-based access to data.
• Business analysis: to elicit the needs of different business units and departments.

Taking into account that big data and data science skills are in short supply, it will take Colin many months to find all the required roles to fill in the talent gaps.

Lengthy development and adjourned benefits

It may take Prod&Sell about 2 years to develop the solution with all the analytic capabilities they want. This presupposes a long transitional period when the data analytics team will have to split their efforts between continuing with the existing practices and elaborating on new ones. Even if Colin’s team smartly chooses among available software development life cycle models, they will be able just to shorten the transitional period, not to get rid of it. This means that routine tasks will anyway retard the achievement of strategic goals, which seems to make the efforts on the data analytics team development futile. As a result, the C-suite may eventually decide to abandon the idea of further growth.

A hard choice among multiple organizational options

Prod&Sell will have to decide whether their analytics will be centralized or decentralized and, correspondingly, what the place of the altered analytics department will be in the organizational structure (what structural units it should be subordinate to – finance, marketing and sales, or each strategic business unit). Making a strategic mistake at this stage can result in the need for further restructuring and refocusing the analytics department in the future.

But overall, things aren’t that bad

Though the situation the article describes is rather gloomy, by no means I’m trying to dissuade you from developing an in-house analytics team. My message is ‘Be prepared for challenges and elaborate on the ways to overcome them before they happen’. For example, you can invite consultants to cover talent gaps and transfer knowledge or outsource a part of analytics, which promises most of the problems.

The fact that small companies generate less data doesn’t mean big data initiatives should be set aside for them. From our big data services practice, we confirm that small companies are particularly capable of acting quickly on acquired data-driven insights.

However, when starting a big data initiative, you should remember that big data analysis will result in meaningful insights only if appropriate data sources are chosen. And the main concern of a small company is that their limited budget put some restraint on this selection process. To help you choose wisely, our consultants present three insightful internal and external data sources for you to consider.

CRM

CRM systems contain data gathered at each customer “touchpoint” – marketing, sales, and after-sales service. Integrated with various software (website, helpdesk, ecommerce, etc.), CRM becomes a solid foundation where the aggregated all-round customer data is stored. By analyzing this data, you can:

• Get a 360-degree customer view to deliver a more personalized experience. When you clearly understand your customer journeys across all channels, you can use every opportunity to convert inquiries into sales by enabling your customer-facing employees to make quick informed decisions.
• Modify your products/services or create new ones that meet customer needs better by analyzing customer complaints, requests for support and cases of returned products.

Your website

Website analytics plays a crucial role in improving your online presence and provides you with insights on how to meet the lead generation goal. With this data source, a big data solution can help you:

• Tailor your landing pages based on where your visitor traffic comes from.
• Improve customer journeys by understanding what affects customer behavior and optimizing your website to customer needs.
• Raise conversion rates through measuring and analyzing current conversion figures and identifying new conversion opportunities.
• Update your marketing campaigns by assessing their performance.

Social media

Though internal data sources are insightful, analyzing internal data only is not enough even for a small business. If you don’t use this opportunity, you miss out on revealing social trends and outperforming competitors. Social media channels contain a wealth of data that your existing and perspective customers share both intentionally and unintentionally. Social media analytics helps you:

• Run better influencer marketing and keep up with the trends in your business sphere by identifying customer sentiment.
• Generate a better strategy to drive your brand recognition through analyzing your customers’ feedback, which opens up an opportunity to convert your followers into customers.

See how small business uses data sources

According to IBM research, 62 % of retailers leverage big data analytics to keep ahead of the competition. Among them are small companies like BaubleBar and Emitations, which deploy big data analytics to stay on top of trends. By tracking customers’ characteristics (demographic, psychographic ones), analyzing their buying behavior and feedback, these accessories brands gain an in-depth insight into the popular choices of their customers. They compare this data with social trends in fashion to identify patterns and correlations that help design their offering and define their marketing strategy.

Be ready to go big data

The global business intelligence and big data analytics market is forecast to grow, making it hard to stay out – the opportunities of big data are tempting.

Are you ready to make the most of your big data? ScienceSoft can help you choose the most suitable option for your big data initiative, be it in-house implementation or outsourcing big data analytics.

Let us define your big data options

Editor’s note: Is ‘a big data warehouse’ just another buzzword for you? Read on to discover the role of the big data warehouse in a big data solution and have a look at ScienceSoft’s offer in big data services to learn how we help our customers leverage big data potential.

ScienceSoft’s experts in DWH services refer to the term ‘big data warehouse’ in their everyday practice. In the article, I’ll explain what they mean by the big data warehouse and how it is different from the traditional (enterprise) DWH.

Big data warehouse vs. traditional DWH

The big data warehouse is a central storage component of the big data solution’s architecture, and the difference with the traditional DWH lies in:

Data type

The traditional DWH stores homogeneous data only: records from CRM, ERP, etc. The big data warehouse is a universal storage repository: it stores both traditional data and heterogeneous big data – transactional data, sensor data, weblogs, audio, video, official statistics, and others.

Data volume

Enterprise data warehouses cannot deal with a very large volume of data (typically, they store terabytes of data). As for big data warehouses, they allow storing petabytes of data and beyond. Surely, such volumes need proper management, and here we share our experience on how the properly chosen technology stack can tackle this task for our customers.

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Approach to data quality

The traditional DWH demands data to be consistent, accurate, complete, auditable, and orderly.

When speaking of big data quality, it is impossible to meet the above requirements, and, luckily, there is no need to. Data experts set minimal satisfactory thresholds to refine data in the big data warehouse to the ‘good-enough’ state. These thresholds vary depending on a particular task. Let’s take requirements for big data completeness, for example. When analyzing shopping trends in social media, the 100%-data completeness is not really needed – we can define customer sentiment during the autumn season without the two-day amount of data. However, in case of IoT analytics in oil and gas, – the minimal satisfactory thresholds will be higher, as without the two-day amount of data you can miss some important patterns, which can result in machinery breakdowns or oil spillages.

Technology stack

Among the technologies utilized in the traditional DWH are Microsoft SQL Server, Microsoft SSIS, Oracle, Talend, Informatica, etc.

The big data warehouse employs specific technologies that can deal with storing huge volumes, close-to-instant streaming and parallel processing of big data: HDFS, Apache Cassandra, HBase, Amazon RedShift, Apache Spark, Hadoop MapReduce, Apache Kafka, etc.

Insights

The big data warehouse architecture allows advanced AI-based analytical technologies like machine learning. By analyzing big data from multiple sources, companies can have deeper insights on enhancing business processes, make accurate predictions and generate prescriptions.

The enterprise data warehouse also employs analytics, but due to the limited amount of stored data, the above-mentioned advanced technologies, which are very data-hungry, cannot be embraced to the fullest. Thus, the analytics results only describe what happened and diagnose the reason for the outcome.

Data access

Although both DWH types pursue the common goal – delivering intelligence to decision-makers, the big data warehouse goes further as it allows rapid reporting to be available across the organization. That way, the insights are granted to a larger number of decision-makers.

It’s time to go big data

A big data solution can’t go without a big data warehouse. What is more, you may need to have it augmented with a data lake. However, if you don’t feel like diving into technical details on the way to your big data solution that addresses your business objectives, you are welcome to ask ScienceSoft’s team for a customized solution.

Editor’s note: Big data analytics has already taken root in the energy industry. In this article Alex Bekker, Head of Data Analytics Department at ScienceSoft, describes how exactly big data analytics can drive value in the electric power sector. In case you’re one of those considering the launch or improvement of a big data solution, you are welcome to explore ScienceSoft’s offering in big data services.

Electric utilities go for smart grids with advanced metering infrastructure and big data capabilities to get strategic insights that would foster efficient energy use. Based on the experience gained from our cooperation with electric utilities, I will show you three practical examples of how big data analytics augments the energy industry.

Fault detection and predictive maintenance

It’s not news that failures in the energy industry equipment may result in catastrophic power blackouts and vast sums of money spent on new assets, restoration works and energy losses. To avoid or minimize such outcomes, I advise developing an efficient equipment monitoring and predictive maintenance approach, the key technologies of which are smart meters and big data. As well as sensors, whose operating principle is described by my colleague, Alex Grizhnevich, in the article dedicated to IoT-based predictive maintenance, smart meters generate all kinds of equipment state data to communicate disturbances, their localization and fault types to the utility in real time. It allows electric utilities to employ advanced big data technologies to detect disturbances early enough to avoid breakdowns and costly downtimes.

Do you want to benefit from predictive maintenance to extend energy equipment’s lifespan and avoid costly breakdowns?

ScienceSoft’s experts can share our proven big data best practices to help you avoid unplanned downtimes due to equipment failures.

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Electric power quality

Electric power quality influences the safe operation of a power grid and consumers’ satisfaction. Fortunately, big data software goes far beyond detecting disturbances a posteriori. For example, we at ScienceSoft offer our customers to implement continuous power quality monitoring to create “an early warning system” empowered with deep learning and pattern recognition algorithms. With this system, you can analyze all the information related to power quality, detect and classify deviations from the norm appearing in power grids quickly and accurately. Once the deviation is classified, it is possible to determine its causes and take measures to prevent it, avoiding downtimes and production losses.

Load management

Advanced big data analytics methods enable accurate load forecasting, which is the basis for effective energy management. Here, let me set an example of how data science can help electric utilities forecast the load and save the investments. To enable that, they partition the geographical area according to local weather information and use data from smart meters. Smart meters, constantly collecting data, feed the AI technologies with data to identify consumers’ typical behavior, match behavior patterns with historical data on weather conditions and make accurate predictions about customers’ real-time behavior under certain weather conditions. And utilities are not the only ones to win in this situation: in combination with in-home displays and programmable communicating thermostats, electric power users obtain the information that can encourage them to initiate a change in the energy consumption, which advances the era of conscious energy consumption.

It’s time to secure the energy gains!

Considering the benefits that predictive maintenance, power quality monitoring and load management bring into the energy industry, many electric utilities have already leveraged big data analysis. The use cases I’ve outlined here are by no means exhaustive. If you’d like to learn more about the value, which big data analytics may bring, you are welcome to explore the overview prepared by my former colleague Olga Baturina.

A big data journey may be long and full of risks to overcome. But I’m convinced, with the right strategy at hand, the result is always worth it. If you are not sure where to start or think your current big data solution leaves something to be desired, ScienceSoft’s data analytics team will be glad to help, just let us know.

Editor’s note: In the article, Irene explains why measuring sales performance is so important for sales growth and shares three examples of how a company can facilitate this process. If you feel that you need assistance with your sales analytics, consider turning to ScienceSoft’s data analytics consulting services.

Out of major industries, retail is one of the most susceptible to changing consumer needs and the dynamic economic environment. To meet your sales goals month after month in such circumstances, you need to constantly assess your company’s performance and make quick adjustments.

In this article, I’ll share with you some examples of sales metrics you need to track to stay in the know about your sales performance and three options to facilitate their analysis.

What sales metrics to analyze?

Sales metrics are quantifiable measures that help you assess the effectiveness of a salesperson, a sales team, the whole organization (sales productivity metrics), the sales process or its aspect (sales performance metrics) against set objectives.

Due to the abundance of all-rounded sales data, many of ScienceSoft’s clients face the dilemma of which sales metrics they need to track when there are dozens available. As analyzing the right metrics is crucial for spotting critical information about your sales process, I advise companies to carefully choose individual KPIs based on their industry and short-term and long-term business goals.

Among the common sales metrics I usually recommend tracking are:

This sales performance measurement shows the percentage of leads that convert into customers, and it is used for forecasting your revenue objectives. The conversion rate also measures the effectiveness of your sales activities: if the win rate is increasing with the same or higher number of closed deals, sales team performance is improving.

To calculate the average deal size, you have to divide the total revenue from the closed deals by the number of those deals. This metric is particularly important for those companies who plan to move upmarket – the bigger the deal size, the closer they are to enterprise-level contracts.

To understand the profitability of a sale, you should calculate your sales-to-cost ratio. For that, you compare the revenue you earn from the deal to the cost of acquiring it. Should you find out that the revenue you earn from securing a deal is only enough to cover the expenses, some urgent actions are required – searching for the ways to reduce costs, shortening your sales cycle, rethinking your target market, etc.

Your sales funnel consists of a certain number of stages, each of which can lead to winning or losing a sale. Only by monitoring your stage-by-stage conversion, you’ll be able to define at which stage your leads are likely to quit business with you. The quicker you define and eliminate weak points in your conversion funnel, the higher your win rate will be.

How to track and analyze sales metrics?

When the key sales metrics to analyze are defined, the challenge arises to provide business users with seamless and timely access to their analysis. For that, you have to develop an effective sales analysis environment and get the right software in place. Here are some common in-house options:

Among Excel advantages, I may point out its sufficiency as a personal solution for analyzing historical data, plus it is affordable and comparatively easy to master. However, the tool is not effective for collaboration and forecasting. What is more, all the information has to be entered into Excel manually, which is extremely time-consuming and can result in numerous errors. Consequently, the accuracy of the analysis will be greatly impaired.

• Analytics capabilities of your CRM/sales management tool

A CRM system compiling all-rounded information about your customers from a variety of sources can help you derive actionable insights crucial for achieving your sales goals. For example, you can learn which communication channels bring more value, the cost of acquiring customers, best up-selling and cross-selling opportunities, and much more. But to be fair, not every CRM software is capable of that. Your CRM can be considered effective for sales analysis if it has:

• Vast analytical capabilities that facilitate customer segmentation to optimize marketing and sales processes, forecast challenges approaching through the sales pipeline, etc.
• Powerful integration capabilities to connect to data sources (ecommerce platform, social media, store software, etc.) needed for conducting comprehensive sales analysis.
• Setup data management procedures to ensure high data quality.
• Intuitive visualization to allow getting a visual snapshot of the metrics.

For a real-life example of utilizing CRM capabilities for sales analytics, have a look at one of our projects, in which ScienceSoft implemented and customized a CRM system to allow a multibusiness company to gain greater visibility into daily operations.

Not Sure About the Most Feasible Way to Conduct Sales Analysis?

ScienceSoft’s experts are ready to evaluate your existing analytical environment to define your best sales analytics solution.

Find a fitting analytics option

• Self-service business intelligence solution

With a well set-up and tuned self-service BI solution, you get:

• A central repository of aggregated and cleansed sales data from integrated corporate applications, as well as from external data sources. That way, you can connect to all data required for getting a holistic view of the whole sales cycle.
• Profound analytical capabilities powered by data science and machine learning, which are applied to your sales data to find the answers to most deliberate questions. Besides understanding the reasons behind certain sales performance, you can get detailed recommendations on how to enhance the performance of your sales team, what stages of your sales pipeline require immediate adjustments, and what corrective actions to your sales funnel will bring maximum ROI.
• Self-service visualization and reporting functionality, which bring sales analytics at your fingerprints. With informative and easy-to-digest reports and dashboards, you can track your key sales metrics in real time to conduct benchmarking, define hurdles to hitting your sales quota and obtain insights into how to improve your sales team’s performance and the whole sales process. To see how it works in practice, watch our BI demo.

The key to successful selling

I’m sure that the key to remaining competitive on the market with timely and accurate decisions is the right analytics solution. The article outlined some of the in-house solutions for tracking and analyzing sales metrics. But surely, an in-house solution is not the only way to go. If having an analytics solution inside your organization is not an option, you can always opt for outsourcing. If you feel that you need help with defining your most fitting sales analytics option, don’t hesitate to reach out to ScienceSoft’s consultants.

Editor’s note: Desperately searching for data science talent? ScienceSoft’s data science consultants are ready to help you both in word and deed. Read on to learn what types of data scientists we differentiate and get the required competence from us.

Let’s face reality: data science unicorns do not exist. If you are searching for a data scientist who can do anything from defining your data science-related business needs regardless of the field you operate in to building a complex deep neural network with the same mastery, you are at high risk of never finding such a person. In data science, specializations rule.

With that, you can find numerous approaches to classifying data scientists, which can include from 2 to more than 10 data science jobs/specializations. At ScienceSoft, we also distinguish among different types of data scientists having a professional data science team on board. Still, being committed to keeping things simple, we recognize just 2 types of data scientists: analysts and technicians. Let’s get to know their core responsibilities better.

Data scientists – analysts

Data scientists – analysts are proficient in translating business needs into the design of data science solutions, as well as interpreting the findings achieved with the help of these solutions back to the business. To do this successfully, they should have a solid grasp of industries they serve, as well as domain knowledge like supply chain management, predictive maintenance, and quality management.

The core responsibilities of data scientists – analysts are:

• Analyzing business needs that require data science, like forecasting, optimization, root cause analysis.
• Managing the quality of raw data.
• Preparing the data required to train a machine learning model (for example, augmenting data and reducing noise).
• Defining factors that influence the accuracy of predictions (for example, defining that for the purpose of demand forecasting, a machine learning model should analyze latest sales trends, seasonality, patterns specific to each SKU, as well as the influence of promotions).
• Exploring data and interpreting results (i.e., making sure that the model differentiates signals from noise).
• Building reports and dashboards to visualize the analysis findings.

To get a real feel about the work this type of data scientists performs, check out one of the projects from ScienceSoft’s portfolio that illustrates their competence: Data Science for an Automated Trading System.

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Data scientists – technicians

Data scientists – technicians are adept at converting a data science concept into a robust solution. They work with mathematical formulas and code, making machine learning algorithms consume data and produce the relevant output. For example, in one of ScienceSoft’s latest data science blog posts, our Head of Data Analytics Department explained in detail how a deep learning model predicts the optimal inventory level based on historical sales data.

The core responsibilities of data scientists – technicians are:

• Choosing the optimal machine learning algorithm among the available options.
• Designing and implementing machine learning (including deep learning) models.
• Choosing relevant activation and optimization functions.
• Tuning the models’ hyperparameters.
• Training and retraining the models.

Check a real-life project from ScienceSoft’s practice to see how our data scientists designed and implemented a convolutional neural network to enable automated medical diagnostics: Development of a Brain Tumor Localization Application.

No need to choose between the 2 types. Get both!

ScienceSoft has gathered a pool of data science professionals (both analysts and technicians) who are ready to drive and back up the improvements that your business longs for, no matter the area. With us, you’ll be able to increase your production efficiency and sales effectiveness, optimize your supply chain, predict customer behavior, and offer your customers with an impeccable experience. If you are still in doubt as to which type of data scientists your business needs, hesitate no more! ScienceSoft’s projects show that our customers needed the competence of both roles.

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In 2016, PepsiCo launched their 2025 Sustainability Agenda, a detailed overview of PepsiCo’s business model that was to focus on “products, planet and people”. PepsiCo supported each of the strategic goals with KPIs and defined targets for them. Recently, the company published their Q2 2017 results, at which we will look from the perspective of BI consulting.

PepsiCo’s KPIs to build the portfolio of healthier products

We’ll focus on one of the pillars of 2025 Sustainability Agenda – PepsiCo’s healthier product portfolio. Here is how this strategic initiative is expressed in KPIs and relevant goals that the company aims to reach by 2025:

These KPIs are specific, measurable, relevant and time-bound. In other words, they are in line with the well-known SMART approach. However, will these KPIs contribute to the company’s success?

Can PepsiCo boast of its success?

If at first PepsiCo’s decision to differentiate and to offer healthier products might have cast some doubts upon sceptics, the doubts should have lessened after PepsiCo published their Q1 2017 results. Recently, the company announced the Q2 2017 results showing organic revenue growth of 3.1%.

Can now business intelligence consulting practitioners add PepsiCo to the list of best practice examples of how to successfully translate a corporate strategy into KPIs? So far, it seems too early for that. Pepsi’s efforts to make their product portfolio healthier are evident: the company divided their brands into good-for-you and fun-for-you categories, as well as adopted the practice of indicating product content clearly on packaging. Still, it’s difficult to say how much PepsiCo progressed in achieving their KPIs.

Undoubtedly, PepsiCo oversees how they advanced in KPIs. What’s more, the company signed an agreement with Partnership for a Healthier America to monitor their progress against previously outlined goals. However, the results have not been published yet.

So far, we can suppose that good financial results and the progress in KPIs are interrelated, as in April 2017, the company reported that more than 45% of PepsiCo’s revenue had come from its “guilt-free” beverage and snack division. “Guilt-free”, a term that refers to PepsiCo’s product portfolio, means beverages with fewer than 70 calories per 12 oz. and snacks with lower amounts of salt and saturated fat.

To sum it up

PepsiCo’s corporate strategy, targets and KPIs are transparent and clear. However, half-year results are not sufficient to state that PepsiCo’s efforts to satisfy the needs of health-conscious customers are paying off. We’ll keep track on this topic in our Business Intelligence blog to finally understand if PepsiCo’s case is a good example of translating the corporate strategy into KPIs.