How to become big data – data analyst

Anyone who works in the tech industry is aware of the rising demand of Analytics/ Machine learning professionals. More and more organisations have been jumping on to the data driven decision making bandwagon, thereby accumulating loads of data pertaining to their business. In order to make sense of all the data gathered, organisations will require Big Data Analysts to decipher the data.

  Data Analysts have traditionally worked with pre formatted data, that was served by the IT departments, to perform analysis. But with the need for real time or near-real time Analytics to serve end customers better and faster, analysis needs to be performed faster, thereby making the dependency on IT departments a bottleneck. Analysts are required to understand data streams that ingest millions of records into databases or file systems, Lambda architecture and batch processing of data to understand the influx of data.

Also analysing larger amounts of data requires skills that range from understanding the business complexities, the market and the competitors to a wide range of technical skills in data extraction, data cleaning and transformation, data modelling and statistical methods.

Analytics being a relatively new field, is struggling to resource the market demands with highly skilled Big Data Analysts. Being a Big Data Analyst requires a thorough understanding of data architecture and the data flow from source systems into the big data platform. One can always stick to a specific industry domain and specialize within that, for example Healthcare Analytics, Marketing Analytics, Financial Analytics, Operations Analytics, People Analytics, Gaming Analytics etc. But mastering the end-to-end data chain management can lead to plenty of opportunities, irrespective of industry domain.

The entire Data and Analytics suite includes the following gamut of stages:

• Data integrations – connecting disparate data sources
• Data security and governance – ensuring data integrity and access rights
• Master data management – ensuring consistency and uniformity of data
• Data Extraction, Transformation and Loading – making raw data business user friendly
• Hadoop and HDFS – big data storage mechanisms
• SQL/ Hive / Pig – data query languages
• R/ Python –  for data analysis and mining programming languages
• Data science algorithms like Naive Bayes, K-means, AdaBoost etc. – Machine learning algorithms for clustering, classification
• Data Architecture – solutionizing all the above in an optimized way to deliver business insights

The new age data analysts or a versatile Big Data Analyst is one who understands the complexity of data integrations using APIs or connectors or ETL (Extraction, Transformation and Loading), designs data flow from disparate systems keeping in mind data security and quality issues, can code in SQL or Hive and R or Python and is well acquainted with the machine learning algorithms and has a knack at understanding business complexities.

Since Big Data and Analytics is constantly evolving, it is imperative for anyone aiming at a career within the same, to be well versed with the latest tech stack and architectural breakthroughs. Some ways of doing so:

• Following knowledgeable industry leaders or big data thought leaders on Twitter
• Joining Big Data related groups on LinkedIn
• Following Big Data influencers on LinkedIn
• Attending events, conferences and seminars on Big Data
• Connecting with peers within the Big Data industry
• Last but not the least (probably the most important) enrolling in MOOC (Massive Open Online Course) and/ or Big Data books

Since Analytics is a vast field, encompassing several operations, one could choose to specialise in parts of the Analytics chain like data engineers – specializing in highly scalable data management systems or data scientists specializing in machine learning algorithms or data architects – specializing in the overall data integrations, data flow and storage mechanisms. But in order to excel and future proof a career in the world of Big Data, one needs to master more than one area. A data analyst who is acquainted with all the steps involved in data analysis from data extraction to insights is an asset to any organization and will be much sought after!

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Continuous delivery of Analytics

 

I am biased towards Analytics not only because it is my bread and butter but also my passion. But seriously, Analytics is the most important factor that helps drive businesses forward by providing insights into sales, revenue generation means, operations, competitors and customer satisfaction.

Analytics being paramount to businesses, the placement of it is still a matter of dispute. The organisations that get it right and are using data to drive their businesses, understand fully well that Analytics is neither a part of IT nor a part of business. It is somewhere in between, an entity in itself.

The insights generated from Analytics is all about business drivers:

• Performance of the product (Product Analytics)
• How well is the product perceived by customers (Customer Experience)
• Can the business generate larger margins without increasing the price of the product (Cost Optimisation)
• What is the bounce rate and what causes bounce (Funnel Analytics)
• Getting to know the target audience better (Customer Analytics)

While the above insights are business related and require a deep understanding of the product, online marketing knowledge, data stickiness mastery and product management skills, there is a huge IT infrastructure behind the scenes to be able gather the data required and generate the insights.

To be able to generate the business insights required to drive online and offline traffic or increase sales, organisations need to understand their targeted customer base better. Understanding customer behaviour or product performance entails quite a number of technical tasks in the background:

• Logging events on the website or app such as registration, add to cart, add to wish list, proceed to payment etc. (Data Pipelines)
• Having in place a scalable data storage and fast computing infrastructure, which requires knowledge about the various layers of tech stack
• Utilising machine learning and AI to implement Predictive Analytics and recommendations
• Implementing data visualisation tools to distribute data easily throughout the organisation to facilitate data driven decision making and spread data literacy

As is the case, Analytics cannot be boxed into either Tech or Business. It is a conjoined effort of both business and tech to understand the business requirements and translate the same into technically implementable steps. Many organisations make the mistake of involving Analytics at the end stage of product or concept development, which is almost a sure shot fiasco. Analytics needs to be involved at every step of a product development or customer experience or UX design or data infrastructure to make sure that the events, the data points that lead to insights, are in place from the beginning.

Delivering Analytics solutions is a collaborative effort that involves DevOps, data engineers, UX designers, online marketeers, social media strategists, IT strategists, Business Analysts, IT/Data architects and data scientists. A close co-operation between tech and business leads to continuous delivery of smarter and faster automations, enhanced customer experience and business insights.

Build. Measure. Evaluate. Optimise. Reevaluate.

 

 

AARRR Metrics for a Fintech

 Lets assume this is a case study for a Fintech company’s KPI definition.

Company X is a Fin Tech company providing payment solutions to SME and small businesses via mobile app, card reader and NFC. Company X solutions provide bookkeeping and analytics features to its customers by means of tracking its product usage and events.

Tracking mobile app usage and web sites are done by using web and mobile analytics tools such as Localytics, Flurry, Google Analytics, Tealium, Xiti etc. But in some cases the data from the analytics tools are not enough to deduce conclusions and hence require additional data from various systems such as CRM, Financial transaction systems, CMS and inventory control systems. Due to the need for blending data from disparate systems, a data strategy needs to be defined and a robust and scalable data architecture needs to be in place.

I would like to provide two relevant blog posts from my own blog that point to the concepts of growth hacking and data blending.

Data Value Chain

Growth Hacking

KPIs

Data monetization for the growth of businesses, entails tracking user behavior both online and offline to optimize products and processes. A list of KPIs or metrics to measure product usage and means of revenue generation are used as a guideline for data monetization efforts. Whether it is to assess global performance of a site, measure the impact of a specific campaign or product feature change, a set of indicators will be needed to focus on the changing parameters.

There are 5 metrics defined by Dave McClure : Acquisition, Activation, Retention, Referrals, Revenues or AARRR also known as the pirate metrics that serve as a good indicator of business growth.

For each of the metric area there are several KPIs defined. For each of the KPIs there are again 4 essential components or ways of analyzing:

• Data points – Data points are the points in the app or site that generate interesting insights about the business in question. It could be individual features in the product or events.
• Funnels – Setting up funnels ensures tracking all the steps that lead to completion of a particular process on the site or app like tracking steps that lead to an online payment page or the steps that lead to a signing up for a newsletter.
• Segmentation – Segmenting the potential and existing customer base to be able to understand their wants and needs in order to be able to serve them better, which is a means of revenue generation. Segmentation can be
• Behavioral – Users who spend lot of time on the site or app, frequently login or rarely login, browsers, visitors that leave without making purchases or visitors that make purchases
• Technical – The browsers used, the OS versions, devices used and if the users have saved the site as a bookmark or enter the site through search engines or social networks
• Demographics – Clustering users based on their age, gender, location etc.
• Cohorts – Cohorts are also a type of segmentation but more from a time series perspective to be able to compare data sets at different points of time. For example checking trends or shopping behaviors at different points in time.

The pirate metrics for product usage can be broadly classified as below:

Acquisition

The process of acquiring customers, which would mean tracking new customers that visit the site or download the app or search the product. The KPIs for acquisition would include all the metrics that indicate a growth or changing trends:

• Number of unique visitors
• %mobile traffic
• %web traffic
• % traffic from social networks
• % traffic from search engines
• Number of app downloads
• Visit trends
• Page view trends
• App Download trends
• New User Account Creation Rate
• Bounce Rate
• Funnel analysis for conversion
• Number of new customers in the last Month/Quarter
• Number of new customers YoY growth
• Campaign effectiveness – measuring the number of customers signing up or deregistering

 

Activation

When the users have logged in and have started using the product, the usage needs to be tracked to be able to further develop the product for better customer experience.

• Page views
• Time spent on the site
• Hourly traffic
• Seasonal traffic
• Monthly Active Users
• Number of paying customers in the last Month/Quarter
• Number of paying customers YoY growth
• Type of payments
• Types of Merchants (small/SME/seasonal)
• Types of businesses/industry
• Type of most sold items
• Customer Segmentation (Technical, Demographics, Behavioral) to understand customer’s need to use the product to improve product development

 

Retention

Retention is the process of retaining existing customers by continued service leading to customer satisfaction. Measuring the factors that lead to retaining customers is a good indicator.

• Number of returning customers
• Average time for transaction
• of transactions
• Transaction failure rate
• Number of transaction per payment type
• Peak hour
• Peak Season
• Types of Merchants
• Average revenue per Merchant
• Average Revenue per Merchant per branch/Industry type
• Average time taken for deposit to merchants
• Competitor Analysis through web/Facebook crawling
• FaceBook engagement (Likes, Shares, Comments) per Month/week
• Number of Complaints per category of complaint type
• App Store Ratings/Review trends
• Text Analysis for tweets/ Facebook comments
• Number of cash payments Vs Card payments

 

Referrals

When the customer satisfaction index is high, the customers refer the products to others thereby acting as brand ambassadors. Referrals are a means to measure customer satisfaction because customers refer the product only when they are themselves happy with the product usage.

• Number of visits coming from social media
• Number of site entry from Facebook ads
• Number of shares on Facebook
• Text analysis of tweets and Facebook messages

Revenue

One of the most important part of a business is revenue generation as revenue is not only the sustenance factor but an indicator of growth.

• Total Payment Volume
• Total Net Revenues
• Transaction losses
• Net revenue YoY growth
• Net revenue YoY growth per type of business
• Net Revenue per type of card (Master/Visa)
• Sales turnover of customers
• Number of transactions per Month/Quarter
• Number of transactions per type of business
• Number of transactions per Location
• Net revenue per platform (mobile app {ios/Android/ipad}/ card reader/NFC)
• Net revenue per type of merchant
• Average revenue per client
• Average value per transaction
• Peak volume of transactions per hour
• Peak volume of transactions per hour per location per type of business (to be able to suggest to similar merchants about the optimum time and hour of transaction)
• %churn
• %churn per type of merchant/type of business/Month/Quarter
• Average Selling price per type of Merchant per type of business
• Average Selling price per type of Merchant per type of business trends – Monthly/Quarterly/Seasonal
• Number of customers that have applied for loan
• Type of customers (business/demographics) that have applied for loan via Company X

 

Conclusion

Product usage tracking to improve the overall product features and outreach is an iterative process involving several processes like continuous A/B testing, UX strategy, Analytics, ideation and product development. In order to create state of the art products, Company X needs to know who their audience is and how the product will make it easy for businesses to sell. By tracking product usage, the aim should be to learn deeply about the customers’ needs and behaviors to be able to generate great solutions, proactively. Iterating towards the solution that creates the most value by collecting and analyzing data is the key.

Data driving the content

Content marketing has been a marketing approach where relevant and valuable content is used to entice customers. The better the content, more the customer engagement. But what if the companies are distributing content which is irrelevant to their customer base or targeting the wrong audience? The answer ofcourse is the most cliched word in modern times – data!

To get relevant, creative and engaging content out there to a targeted audience, strategic omni channel content marketing needs to be in place. It is imperative to optimize, analyze and curate content according to the brand image and customer demands. It is also equally important to hear the customer opinion in popular social media platforms, to be able to produce content that engages customers.

Optimizing content starts with analyzing content and the consumers of the content. There are innumerable web analytics tools that analyze web traffic. For example Xiti, Optimizely, Clicky, Google Analytics, Marketo and Hubspot to name a few, that can be used to reveal the content that attracts most influx. However, statistical programming languages like R and python are also widely used by data scientists to conduct advanced analytics. To be able to analyz how the content fairs in social media, data regarding the content outreach has to be organized by analyzing the number of likes, shares and comments. Both Twitter and Facebook provide APIs which can be used to extract valuable data to optimize content. For example, by analyzing the frequently used words associated with a particular brand, the sentiment associated with the brand can be determined. Competitive analysis can be carried out by comparing the sentiments associated with brands. Publishing houses are resorting to data journalism to put together related articles in the form of compelling story-telling. Data regarding the articles that attract most traffic at a given point of time is used by publishing houses to manipulate and push the most popular content in real time. Check how The Guardian uses data to narate associated and popular stories on its datablog.

The importance of using data is very significant in increasing the online traffic, however, nothing beats highly creative and engaging content

Programmatic Conversion

Programmatic marketing involves data driven insights to convert prospects into customers. There is more than meets the eye in the case of conversion rate optimization. Some of the deciding factors for conversion are UX design, the landing page, the source of web traffic, content, competitive price of products, good will, social media marketing, effective campaigns and customer engagement. Programmatic marketing entails analsying data at every customer touch point and targeting the consumer with compelling, preferably  personalised, offers. Conversion is not necessarily making a customer shell out money, it could be interpreted as winning customer loyalty by means of signing up for newsletter, downloading whitepapers or trial versions of the product or spending considerable time on the site. This loyalty, in the long run, could result in big wins through persuasion in the form of emails, SMSs, direct contact and targeted recommendations.

Channelizing data about prospects – online behaviour, previous shopping, socio-economic segmentation, online-search, products saved in the online basket, in other words getting to know the customer better to be able to suggest meaningful differences in people’s lives through the products on offer, results in higher conversion rates. It is here that digital convergence is of paramount importance. Digital convergence blends online and offline consumer tracking data over multiple channels to come up with targeted campaigns. Offline tracking through beacon technology is catching up. It is a win-win solution for both the retailer and the consumer providing each with useful information, the consumer, with an enabled smartphone app within a certain distance from the beacon, recieves useful and targeted information about products and campaigns and the retailer gathers data about consumer shopping habbit.

The online experience can be enhanced to reduce the bounce rate by incorporating some of the following design thoughts:

1. Associative content targeting: The web content is modified based on information gathered about the visitor’s search criteria, demographic information, source of traffic, the more you know about the prospect, the better you can target.
2. Predictive targeting: Using predictive analytics and machine learning, recommendations are pushed to consumers based on their previous purchase history, segment they belong to and search criteria.
3. Consumer directed targeting: The consumer is presented with sales, promotions, reviews and ratings prior to purchase.

Programmatic offers the ability to constantly compare and optimize ROI and profitability across mulitple marketing channels. Data about consumer behaviour, both offline and online, cookie data, segmentation data are algorithmically analyzed, to re-evaluate the impact of all media strategies on the performance of consumer segments. Analyzing consumer insights, testing in iterations, using A/B testing contributes to a higher conversion rate. Using data driven methods to gain a higher conversion rate is programmatic conversion and it’s here to stay.

Intelligence Of Things

IoT

IoT – Internet of things, is the science of an interconnected everyday life through devices communicating over WiFi, cellular, ZigBee, Bluetooth, and other wireless, wired protocols, RFID (radio frequency identification), sensors and smartphones. Data monetization has lead to generating revenue by gathering, analyzing customer data, industrial data, web logs from traditional IT systems, online stream, mobile devices and sensors and an interconnection of them all, in other words, IoT. IoT is hailed as the new way to transform  the education sector, retail, customer care, logistics, supply chain and health care. IoT and data monetization have a domino effect on each other which generate actionable insights for business metrics, transformation and further innovation.

The wearable devices are a great way to keep tab on patient heart rates, step counts, calories consumed and burnt. The data gathered from such devices are not only beneficial for checking vital signs but also can be used to scrutinize effectiveness of drug trials, analyzing the causes behind the way body reacts to different stimulus. IoT in logistics, by reading the bar codes at every touch point that track the delivery of products, comparing the estimated with the actual time of delivery, analyzing the reasons causing the difference can help businesses bolster better processes. In Smart buildings, HVAC (heating, ventilation, air conditioning), electric meters, security alarm data are integrated, analyzed to monitor building security, improve operational efficiencies, reducing energy consumption and improving occupant experiences.

IoT is expected to generate large amounts of data from varied sources  with a high volume and very high-velocity, thereby increasing the need to better index, store and process such data. Earlier the data gathered from each of the sources was analyzed in a central hub and communicated to other devices, but the IoT brings a new dimension called the M2M (machine to machine) communication. The highlights of such M2M platforms are

• Improved device connectivity
• API, JSON, RDF/XML integration availability for data exchange
• Flexible to be able to capture all formats of data
• Data Scalability
• Data security across multiple protocols
• Real-time data management – On premise, cloud or hybrid platforms
• Low TCO (total cost of ownership)

The data flow for an end-to-end IoT usecase entails capturing sensor-based data using SPARQL for RDF encoded data from different devices, wearables into a common data platform to be standardised, processed, analyzed and communicated further as dashboards, insights, as input to some other device or for continuous business growth and transformation. Splunk, Amazon, Axeda are some of the M2M platform vendors that provide end to end connectivity of multiple devices, data security and realtime data storage and mining advantages. Data security is another important aspect of IoT, adhering to data retention policies. As IoT evolves, so will the interconnectivity of machine-to-machine platforms, exciting times ahead!

Recommendation Systems

Recommendation systems have changed the way people shop online, find books, movies or music, news articles go viral or find friends and work mates on Linkedin. The recommendation systems analyze the browsing patterns on websites, ratings or most popular items at that point of time or the products saved in ones virtual basket to recommend products. Similarly, the common interests, work skills or common geographical locations are used to predict people, that you might want to connect with on social media sites.

Behind such personalized recommendation systems lie big data platforms including software, hardware and algorithms that analyze customer behavior and push recommended products, in real time. The big data platforms handle both data and event data distribution and computation. Data can pertain to how customers or customers similar to the one in question, have rated products in the past while event data could be tracking mouse clicks that trigger events for example viewing a product and sometimes both of the above need to be combined to be able to predict a customer’s choice. Hence, the recommendation system architecture caters to data storage for offline analysis as well as low latency computational needs and a combination of the two.

The data platform architecture needs to be robust enough to ingest continuous real time data streams into scalable systems like Hadoop HBASE or any other big data data storage infrastructure like AWS Redshift. Apache Kafka is usually used as the messaging system for the real time data stream in combination with Apache Storm. Due to high throughput data redundancy needs to be taken care of, in case of failures. If the real time computation needs to take into account customer data like previous purchase history, preferences, products already bought , segmentation based on socio economic demographics or data from ERP, CRM, in that case either all the systems have to be available online to be able to blend the data in real time or the customer detail data could be mashed up, offline to create Single Customer View and queried in combination with the real time event data.

The valueable assests of any organisation are customers,products and now, data. Machine learning algorithms combine the three assets together to leverage business gains and predictive analytics is imperative in being proactive to customer needs. Some of the algorithms used for recommendation engines are content-based filtering, collaborative filtering, dimensionality reduction, Kmeans and matrix factorization techniques. The challenge is not the data storage, with wide availability of highly scalable data storage platforms, but the speed with which the data needs to be analyzed in case of recommendation systems. The best approach is to combine mostly precomputed data with fresh event data using pre modelled algorithms to push personalised recommendations to the customer interface.

The data value chain

The Consumer Lifecycle

The terms “Data driven” and “Big Data” are the buzz words of today, hyped definitely, but the implications and potential are real and huge! Tapping into the enormous amount of data and associating this data from multiple sources creates a data chain, proving valueable for any organisation. Creating a data value chain consists of four parts: collection, storage, analysis, and implementation. With data storage getting cheaper, the volume and variety of data available to be exploited is increasing exponentially. But unless businesses ask the right questions and better understand the value that the data brings in and be sufficiently informed to make the right decisions, it does not help storing the data. For example, in marketing, organisations can gather data from multiple sources about acquiring a customer, about the customer’s purchasing behaviour, customer feedback on different social media, about the company’s inventory and logistics of product delivery. Analyzing this stored data can lead to substantial number of customers being retained.

A few of the actionable insights can be as follows:

• Improving SEO (search engine optimization), increasing the visibility of the product site and attracting more customers
• CRO (Conversion rate optimization) i.e. converting prospects into sales, by analzying the sales funnel. A typical sales funnel is Home page > search results page > product page > proposal generation and delivery > negotiation > checkout
• Better inventory control systems, resulting in faster deliveries
• Predicting products that a consumer might be interested in, from the vast inventory, by implementing good recommendation algorithms that scan through the consumer behaviour and can predict their preferences
• If some of the above points are taken care of, customer loyalty can increase manifold, based on the overall experience during the entire consumer lifecycle.

Data blending which leads to a Single Customer View and Actionable Insights

Often the focus lies on the Big data technology rather than the business value of implementing big data projects. Data is revolutionising the way we do business. Organisations, today, are inundated with data. To be able to make sense of the data and create a value chain, there has to be starting point and the customer is a good starting point. The customer’s lifecycle with experiences at every touch point defines business growth, innovation and product development. The big data implementations allow blending data from multiple sources leading to a holistic single view of customer, which in turn gives rise to enlightening insights. The data pretaining to customer, from multiple sources, like CRM/ERP/Order Management/Logitics/Social/cookie trackers/Click traffic etc., should be stored, blended and analysed to gain useful actionable insights.

In order to be able to store the gigantic amount of data, organisations have to invest in robust big data technologies. The earlier BI technologies that we had do not support the new forms of data sources such as unstructured data and the huge volumes, variety & velocity of data. The big data architecture consists of the integration from the data sources, the data storage layer, the data processing layer where data exploration can be performed and/or topped with a data visualization layer. Both structured and unstructured data from various sources can be ingested into the big data platform, using Apache Sqoop or Apache Flume, real-time interactive analyses can be performed on massive data sets stored in HDFS or HBase using SQL with Impala, HIVE or using statistical programming language such as R. There are very good visualization tools, such as Pentaho, Datameer, Jaspersoft that can be integrated into the Hadoop ecosystem to get visual insights. Organisations can offload expensive datawarehouses to low cost and high storage enterprise big data technology.

Edited image from Hortonworks

Irrespective of the technical implementation, business metrics such as increasing revenue, reducing operational costs and improving customer experience, should always be kept in mind. The manner in which the data is analyzed could create new business opportunites and transform businesses. Data is an asset and investing in a value chain, from gathering to analyzing, implementing, analyzing the implementations and evolving continuously, will result in huge business gains.

Streamlining the process of processing

The customer expectations are very different, now. Decisions need to be taken in real time, to convert a prospective customer into committing. In an age, where customer seeks instant gratification, organisations that have a longer time-to-market due to cumbersome internal processes, customer loyalty is hard to win. For example, a customer visits your physical store, if you offer a discount at the very first visit, the chances that the customer will revisit your store are high. On the other hand, if you are merely noting customer behaviour which then has to pass through unwieldy processes, later, to mete out a discount coupon, the second time the customer visits your store… if at all, is a thing of the past. The advanced analytics systems now, are able to handle data influx from multiple disparate systems, cleanse and house in the dmp (data management platforms), ready to be queried in real time to cater to predictive and actionable insights, on the fly.

However, if the business methodologies used are not complimenting this speed of data processing, the business will still suffer. The widely used, Lean methodology preaches creating more value for customers with fewer resources. Anything that does not yield value should be eliminated. But organisations need to adapt to only the best of the best practices. Following methodologies by the book, on the contrary, causes bottlenecks. To be able to leverage more out of the Business Analytics systems and solutions, the processes and tools, both, need to be streamlined to create customer satisfaction. A lot of the business intelligence projects take too long to deliver and are inflexible, resulting in the functional business teams procuring BI tools which promise quick wins. The problem with such data discovery tools, apart from creating data silos, are that they lack data governance, hinder data sharing at an enterprise level and increase licensing costs.

It is not a solution to have no business process at all. There needs to be accountability and that comes from business processes. It is a continuous iterative process to find the right balance between processes and the speed of delivering value to keep the costs low and increase the profitability of any business. One size does not fit all and it applies to organisations, as well. Methodologies/processes need to be tweaked, tuned and tailor made for each company. Organisations that try to implement Lean/Agile/Scrum but fail are because they lose the customer focus, some companies do not have a clear strategy in place with employees being assigned foggy responsibilities and lack of communication and this in turn results in the focus shifting from the task at hand to the nitty gritties of such project management methods.

To avoid pitfalls, a clear business strategy needs to be defined specifying business goals in order to maximise gains. The next step is to trim all the processes that lead to this gain.

The bridge between Business and Analytics – Business Data Analyst

The terms business analysis and data analysis have traditionally seemed different. With the increasing amount of data available, stored and the need to analyse that data and gain business insights out of it, a new role, Business Data Analyst is critical. Companies lacking the business data analysis talent pool have a lower ROI and will lose out to companies hiring analytics talent.

Most companies, even today,  have the two competencies separate. Business analysts analyze functional requirements and help translate the same to technical specifications while data analysts are more technical, gathering, cleansing and analyzing data. To increase the analytic throughput of a company it is vital to combine the business and analytic competencies to be able to analyze the data from a business aspect, being able to draw conclusions about consumer behaviour, find trends and accordingly make business decisions with targeted marketing campaigns.

As this is an emerging field, it can be challenging to find right people with both the business acumen as well as analytics skillset. There can be myriad ways to bridge this gap. One strategy can be to create teams of people with direct marketing roles along with data analysts and data scientists to utilise the combined specialised competencies. Another strategy can be to train the management team’s analytical skills or beefing up the business knowledge of data analysts.

No matter which strategies are adapted, the new role of Business Data Analyst is paramount for enabling a company to make the right investments at the right time to yield an ROI. Building a data driven company is more than identifying the right BI tools, it’s about driving business through customer behaviour feedback by analyzing data.