Article | February 19, 2020
With the increasing amount of data in modern businesses, data science has been receiving a lot of attention. A growing number of companies are, nowadays investing in data science researchers and experts to implement technologies like artificial intelligence and machine learning in their organisation in order to derive actionable insights. But, to place such a massive transformation in an organisation, one has to ensure complete business readiness for data science. Although it is interesting to imagine the potential benefits data science can provide for your organisation, it is worth evaluating how much your organisation is prepared to accommodate a team of data scientists.
Article | April 30, 2020
In the present complex and volatile market with data as a nucleus, analytics becomes a core function for any enterprise that relies on data-driven insights to understand their customers, trends, and business environments.
In the age of digitization and automation, it is only sensible to make a move to analytics for a data-driven approach for your business. While a host of sources including Digital clicks, social media, POS terminal, and sensors enrich the data quality, data can be collected along various stages of interactions, and initiatives were taken. Customers leave their unique data fingerprint when interacting with the enterprise, which when put through analytics provides actionable insights to make important business decisions.
Table of Contents:
Business Analytics or Business Intelligence: The Difference
Growth Acceleration with Business Analytics
Business Analytics or Business Intelligence (BI): The Difference
Business Intelligence comes within the descriptive phase of analytics. BI is where most enterprises start using an analytics program. BI uses software and services to turn data into actionable intelligence that helps an enterprise to make informed and strategic decisions.
It’s information about the data itself. It’s not trying to do anything beyond telling a story about what the data is saying.
- Beverly Wright, Executive Director, Business Analytics Center, Georgia Tech’s Scheller College of Business
Some businesses might use BI and BA interchangeably, though some believe BI to be the know-how of what has happened, while the analytics or advanced analytics work to anticipate the various future scenarios.
BI uses more structured data from traditional enterprise platforms, such as enterprise resource planning (ERP) or financial software systems, and it delivers views into past financial transactions or other past actions in areas such as operations and the supply chain. Today, experts say BI’s value to organizations is derived from its ability to provide visibility into such areas and business tasks, including contractual reconciliation.
Someone will look at reports from, for example, last year’s sales — that’s BI — but they’ll also get predictions about next year’s sales — that’s business analytics — and then add to that a what-if capability: What would happen if we did X instead of Y.
- CindiHowson, research vice president at Gartner
A subset of business intelligence (BI), business analytics is implemented to determine which datasets are useful and how they can be leveraged to solve problems and increase efficiency, productivity, and revenue. It is the process of collating, sorting, processing, and studying business data, and using statistical models and iterative methodologies to transform data into business insights. BA is more prescriptive and uses methods that can analyze data, recognize patterns, and develops models that clarify past events, make future predictions, and recommend future discourse.
Analysts use sophisticated data, quantitative analysis, and mathematical models to provide a solution for data-driven issues. To expand their understanding of complex data sets, and artificial intelligence, deep learning, and neural networks to micro-segment available data and identify patterns they can utilize statistics, information systems, computer science, and operations research.
Let’s discuss the 5 ways business analytics can help you accelerate your business growth.
READ MORE: HOW TO OVERCOME CHALLENGES IN ADOPTING DATA ANALYTICS
Growth Acceleration with Business Analytics
1. Expansion planning
Let’s say you’re planning an expansion opening a branch, store, restaurant, or office in a new location and have accumulated a lot of information about your growing customer base, equipment or other asset maintenance, employee payment, and delivery or distribution schedule. What if we told it is possible to get into a much detailed planning process with all that information available? It becomes possible with business analytics. With BA you can find insights in visualizations and dashboards and then research them further with business intelligence and reports. Moreover, you can interact with the results and use the information to create your expansion plan.
2. Finding your audience
You’re right to examine your current customer data but you should also be looking into the customer sentiments towards your brand and who is saying what, and in what parts of the region. Business Analytics offers social media analysis so you can bring together internal and external customer data to create a profile of your customers, both existing and potential. Thus, you have prepared an ideal demographic, which can be used to identify people that are most likely to turn to your products or services. As a result, you have successfully deduced the area that offers the most in terms of expansion and customer potential.
3. Creating your business plan
The real-time interaction with your data provides a detailed map of the current progress as well as your performance. Business Analytics solutions offer performance indicators to find and forecast trends in sales, turnover, and growth. This can be used in the in-depth development of a business plan for the next phase of your thriving franchise.
4. Developing your marketing campaign
With Business Analytics, you’re capable of sending the right message to the audience most eager to try your product/service as part of a marketing campaign. You’re empowered to narrow down branding details, messaging tone and customer preferences, like the right offers that will differentiate you from the other businesses in the area. Using BA, you have gained a competitive edge by making sure you offer something new to your customers and prospects. It enables you to use your data to derive customer insights, make insight-driven decisions, do targeted marketing, and make business development decisions with confidence.
5. Use predictive insights to take action
With analytics tools like predictive analytics, your expansion plans are optimized. It enables you to pinpoint and research about the factors that are influencing your outcomes so that you can be assured of being on the right track. When you can identify and understand your challenges quickly and resolve them faster, you improve the overall business performance resulting in successful expansion and accelerated growth.
READ MORE: WHAT IS THE DIFFERENCE BETWEEN BUSINESS INTELLIGENCE, DATA WAREHOUSING AND DATA ANALYTICS
BIG DATA MANAGEMENT
Article | April 16, 2021
There are many articles explaining advanced methods on AI, Machine Learning or Reinforcement Learning. Yet, when it comes to real life, data scientists often have to deal with smaller, operational tasks, that are not necessarily at the edge of science, such as building simple SQL queries to generate lists of email addresses to target for CRM campaigns. In theory, these tasks should be assigned to someone more suited, such as Business Analysts or Data Analysts, but it is not always the case that the company has people dedicated specifically to those tasks, especially if it’s a smaller structure.
In some cases, these activities might consume so much of our time that we don’t have much left for the stuff that matters, and might end up doing a less than optimal work in both. That said, how should we deal with those tasks? In one hand, not only we usually don’t like doing operational tasks, but they are also a bad use of an expensive professional. On the other hand, someone has to do them, and not everyone has the necessary SQL knowledge for it. Let’s see some ways in which you can deal with them in order to optimize your team’s time.
The first and most obvious way of doing less operational tasks is by simply refusing to do them. I know it sounds harsh, and it might be impractical depending on your company and its hierarchy, but it’s worth trying it in some cases. By “refusing”, I mean questioning if that task is really necessary, and trying to find best ways of doing it. Let’s say that every month you have to prepare 3 different reports, for different areas, that contain similar information. You have managed to automate the SQL queries, but you still have to double check the results and eventually add/remove some information upon the user’s request or change something in the charts layout. In this example, you could see if all of the 3 different reports are necessary, or if you could adapt them so they become one report that you send to the 3 different users. Anyways, think of ways through which you can reduce the necessary time for those tasks or, ideally, stop performing them at all.
Sometimes it can pay to take the time to empower your users to perform some of those tasks themselves. If there is a specific team that demands most of the operational tasks, try encouraging them to use no-code tools, putting it in a way that they fell they will be more autonomous. You can either use already existing solutions or develop them in-house (this could be a great learning opportunity to develop your data scientists’ app-building skills).
If you notice it’s a task that you can’t get rid of and can’t delegate, then try to automate it as much as possible. For reports, try to migrate them to a data visualization tool such as Tableau or Google Data Studio and synchronize them with your database. If it’s related to ad hoc requests, try to make your SQL queries as flexible as possible, with variable dates and names, so that you don’t have to re-write them every time.
Especially when you are a manager, you have to prioritize, so you and your team don’t get drowned in the endless operational tasks. In order to do this, set aside one or two days in your week which you will assign to that kind of work, and don’t look at it in the remaining 3–4 days. To achieve this, you will have to adapt your workload by following the previous steps and also manage expectations by taking this smaller amount of work hours when setting deadlines. This also means explaining the paradigm shift to your internal clients, so they can adapt to these new deadlines. This step might require some internal politics, negotiating with your superiors and with other departments.
Once you have mapped all your operational activities, you start by eliminating as much as possible from your pipeline, first by getting rid of unnecessary activities for good, then by delegating them to the teams that request them. Then, whatever is left for you to do, you automate and organize, to make sure you are making time for the relevant work your team has to do. This way you make sure expensive employees’ time is being well spent, maximizing company’s profit.
Article | February 24, 2020
A US$ 48.3 billion-corporation, the Aditya Birla Group is in the league of Fortune 500. Anchored by an extraordinary force of over 120,000 employees belonging to 42 nationalities, the Group is built on a strong foundation of stakeholder value creation. With over 7 decades of responsible business practices, Aditya Birla Group’s businesses have grown into global powerhouses in a wide range of sectors metals, chemicals, pulp & fibre, textiles, carbon black, cement and telecom. Today, over 50% of its revenues flow from overseas operations that span 36 countries in North and South America, Africa and Asia.The Group Data ‘n’ Analytics Cell (GDNA) is the Big Data and Analytics arm of the Aditya Birla Group created at its centre to strategize and partner with 18+ Group businesses across B2B and B2C domains to deliver on its strategic priorities through the power of AI. The company represents strong analytics and domain expertise drawn from the best-in-class talent from leading global and Indian businesses that leverage cutting edge tools and advanced AI algorithms built on a highly scalable and robust big data infrastructure to mine and act upon petabytes of structured and unstructured data.