Nakala Analytics Ltd - Blog

Data Analytics in Financial Sector

2021-07-24T08:59:35+03:00

Firms and institutions that provide financial services to commercial and retail customers make up the financial industry. It includes a diverse range of businesses, including banks, financial firms, insurance firms, and real estate corporations. The efficiency of a country's financial system is crucial to its economic health. The better the economy of a country, the safer it is. A week financial sector typically means a declining economy.

Role of data analytics in the financial sector

The financial sector is facing many challenges today that most relate to the rapid technological changes. In the modern era, big data is altering business and technical operations in the sector. Numerous financial activities take place every day that leads to uncountable financial transactions. This generates large data in the sector. Therefore, the finance industry needs to exploit this data to meet its current challenges. Below are some of the ways data is revolutionizing the finance sector.

Enhanced analytics: Financial executives can make more competent data-driven decisions. Financial augmented analytics helps in the elimination of human errors. It is more impactful in streaming data management processes, making data accessible to corporate users, and improving on products and services offered.
Personalization: Big data in the financial sector can help in understanding their customers. This enables them to set their priorities right for customer satisfaction.
Edge computing: Edge computing focuses on bringing computing closer to the source of the data as much as possible, enhancing response time.
Business Insights: Insight from data can give a lot of information about an institution. This information is useful when making decisions.
Financial Models: Greater data relevancy generates a stable model with minimum risks. All of this can be easily obtained by implementing a strategy based on data-driven models.

Conclusion

Data has become an extremely valuable resource across all industries, and the financial sector is not an exemption. It has proven to be an essential asset for the growth of many businesses. With data, businesses can understand their customer expectations, the current market trends hence making accurate data-driven decisions. Those running businesses within this sector have no option but to embrace this new revolution to remain competitive.

Alphaeus, Nakala

How Data Analytics will Transform the Agricultural Sector

2021-07-13T09:31:45+03:00

Agriculture is one of society's most important production areas. Agriculture contributes 26% of the Gross Domestic Product (GDP) of Kenya’s economy. The sector is the main source of employment to Kenyans with more than 40 % of the total population and more than 70% of Kenya’s rural people. Hence very crucial to economic growth not only in Kenya but the world’s economy too.

Image by kangbch from Pixabay

Data Analytics in Agricultural Sector

Just like any other sector, Agriculture is facing challenges some of which are more complex and very urgent in nature, like the changing climate and land scarcity. With the growing population, it means there is a need to improve our crop production significantly to cater to this population. Other challenges in the sector include disease and pests invasions, use of outdated technology by farmers, poor infrastructure, reduced soil fertility, Agricultural research and development, and agricultural extension, etc. Farmers and companies in the Agricultural sector need to adopt big data analytics in order to become more productive. Below are some of the ways data analytics benefits the Agricultural sector.

IMPROVED CROP MANAGEMENT.

It is now easier to identify which crop grows best during certain times of the year, season, and other specifics. The use of the historical data from agriculture could uncover all the trends with great efficiency and with minimal errors unlike traditionally where farmers relied on guesswork based on fellow farmers' experiences.

BETTER RISK ASSESSMENTS

Data analytics can help farmers and companies within the agricultural sector to be much aware of the risk. Data could provide insights to detect this.

BIG DATA IoT SENSORS:

Some diseases for example on livestock can spread very easily and faster without Famers realizing the problem. These gadgets can help in collecting data on behavioral change which could be the first sign of illness and this could help farmers to take the right precautions on time.

IMPROVED AGRICULTURAL SUPPLY CHAIN

The supply of Agricultural products presents a number of challenges for both farmers and distributors since food products are perishable. Data analytics can play a key role in the entire supply chain. Using data distributors can identify inefficiencies in their supply chains, retailers can monitor sales and inventory data, customer behavior, hence minimize on waste and stay updated on market demands.

FEEDING A GROWING POPULATION

Using data analytics farmers can have a piece of clear information from rainfall patterns to farm inputs hence enabling them to make smart decisions on what crops to plant to maximize yields and make profits.

How companies have leveraged big data effectively.

Bayer: They developed an application that uses machine learning and artificial intelligence (AI) in weed identification. Helps farmers to identify the species of the weed by uploading photos on their app which matches it against Bayer database.
Digital Transmission Network (DTN): A division of Schneider Electric, provides agricultural information solutions and market intelligence to its customers. Using DTN, farmers and commodity traders can access up-to-date weather and pricing data to better manage their business.

Conclusion

Generally, the agricultural sector has not been associated with data analytics, hence Most farmers have not embraced this new revolution yet. With the current changes experienced globally, farmers need to be up to date with them. With a clear understanding of a problem, farmers can get a solution easily. This is possible through big data analytics, it ca n give insights that can help farmers adjust accordingly to maximize on production hence higher profit.

Alphaeus, Nakala

Data analytics in the hospitality sector

2021-07-12T07:30:01+03:00

What is the Hospitality industry?

The hospitality industry simply is a type of business that focuses on meeting leisurely needs rather than basic ones. Their main goal is customer satisfaction. The hospitality industry can be grouped into different sectors: Food and beverages, Travel and Tourism, Lodging, Recreation, Event planning, Theme parks, and other fields within the tourism industry.

Role of analytics.

Any business that primarily deals with customer service to stay relevant in the business or competitive must learn to adapt to changing customer needs.

Different customers have different sets of expectations, needs, and desires. Therefore, to effectively meet the individual expectations or guests and retain them one has to use advanced solutions like data analytics, hence the role of big data in hospitality is very significant since the sector caters to a large number of people every day. Getting the right insights gives you an advantage in a competitive business environment like this.

The hospitality sector has been faced different challenges before and remains a problem to some businesses in this sector. Big Data comes in as a solution to this. Below are some of the ways that big data can benefit the hospitality sector:

Revenue Management

Using the data gathered from within and information available online businesses can use big data for revenue management strategy particularly for predictive analysis. This allows owners to more accurately anticipate levels of demand.

Targeted Marketing

Big data help in understanding the characteristics that distinguish various customers in the sector from hotel guest to travelers and their preferences. With knowledge of each group's behaviors and expectations, marketing can be tailored to meet this hence greater conversion rates.

Customer/guest experience

Analyzing service usage data, feedback from customers on social media, reviews posted on websites, and other related information can help the customer service team identify significant trends of customer opinions, discover their strengths and weaknesses, and know where to improve as a business. This in return will address guests' concerns hence attracting more customers.

Competition Analysis

With the right insight on your competitors’ shortcomings and the services they offer. Businesses can easily identify where to capitalize for it to remain relevant in the market today. All this information is available online on social media forums, travel publications, and review insights.

Conclusion

Over recent years, data analytics has proven to be a powerful game-changer in the Hospitality sector. Businesses within this sector that have turned to advanced analytics have proven to be more competitive in this field. An example is Marriott they use Group Pricing Optimiser (GPO) that enables them to sell the way a specific set of customers want to buy. In Kenya, the majority in the hospitality sector are yet to adopt advanced analytics into their businesses. The primary goal in hospitality is customer satisfaction. To achieve this, one needs to have a clear understanding of the customer, which is possible using Big data analytics.

For help, contact us https://nakala-analytics.co.ke/contact-us

Role of Data Analytics in Health centers

2021-07-12T07:17:11+03:00

The health sector comprises businesses whose goal is to meet the healthcare needs of individuals or communities. Some of the sectors in the healthcare industry include medical services, hospital supplies and manufacturers of medical equipment or drugs, medical insurance or facilitate the provision of healthcare to patients.

The Healthcare sector is facing many changes that are posing new challenges in the medical organizations, for instance, technological innovations. Some of the challenges facing this industry include information and service integration, cybersecurity, Invoicing, and payment processing, among others. Data analytics comes in as a solution to all these. Use of Data Analytics in the health sector can help in the realization of reduced treatment costs, more effective diagnostic, prevention of avoidable outbreaks, and improved healthcare services across the industry.

Role of data analytics in the health care sector.

The industry comprises different sectors that deal with different services. Therefore, how data analytics is used to improve these sectors can be slightly different. For instance, Hospital that deals with medical services may benefit differently from medical equipment/drugs manufacturers. The Health center’s goal is to provide better health care services while medical equipment manufacturers and medical insurance companies' primary goal will be customer satisfaction by providing the best services.

Role of Data Analytics in Health centers

Data analytics can improve health outcomes for individuals and reduce the time spent on administrative tasks in healthcare.

Automated appointments: Automation in the Healthcare providers optimizes patient outcomes.
Diagnostics and prevention at an early stage: Health monitoring applications for example symptom checker applications can help individuals in detecting health issues on time and seek diagnoses on time.
Track health of a patient: With the use of big data, it is becoming easier to control chronic illnesses and infectious diseases.
Predictive analytics: Helps in managing hospital staff through demand forecasting, predict the cost for treatment, among others. Predictive analyses help in improving capacity utilization for the health sector.
Diagnostic and therapeutic procedures that are more successful: using the historical data for the previous prescriptions can help in determining the best treatment for a particular condition.

Role of Data analytics for businesses in the health industry

Data Analytics in healthcare is a crucial tool for improved operational efficiency and customer experience.

Automating processes: Using machine learning and artificial intelligence, healthcare providers can streamline workflows and digitalize processes, hence improving their operational efficiency.
Insight into the business world: This helps organizations to make more data-driven decisions and healthcare providers gain more visibility into financial operations, automated reports, and much more.
Fraud and abuse prevention: big data analytics can help in detecting fake documents along with predicting the probability of a customer will churn.
Improved customer service: Use big data analytics helps to understand the trends and customer demands. This helps businesses to adjust accordingly to meet market demands hence can help in retaining customers.

Conclusion

With improved healthcare services new challenges are being experienced in this industry. If there are better healthcare services it means the life expectancy of humans will increase hence healthcare system will handle more patients with complex needs. Therefore, there is a need to come up with a system that focuses on long-term care instead of fulfilling short-term requirements. For the health industry to achieve it must leverage big data analytics into its operations.

Alphaeus, Nakala

The Modern ways to Effective Data Audits

2021-04-06T16:04:41+03:00

90% of all innovation projects fail because companies don't take the time to figure out if their current data assets will help them meet their strategic goals. Data audits can shorten your path to data value extraction by uncovering issues such as data accuracy, security, breadth and reap the benefits of confronting problems head on. Nakala Analytics, a leading data analytics startup company from Kenya, has been ranked top among 482 startups that are transforming the data audit space using data analytics.

Today's leaders are in charge of companies that operate in overly complex, diverse, and internationally competitive environments. The world is becoming more data-driven by the day in this era of data. A data audit for data innovation using technology aims to quickly diagnose issues and make suggestions in order to support preparation, collection, processing, use, delivery, and storage of data in a way that encourages affluent innovation and achieves anticipated ROI.

Why a data audit?

A data audit for data innovation from Nakala aims to diagnose and then provide recommendations that make it easier to obtain, process, use, secure, transmit, and store data in a way that encourages affluent innovation and achieves anticipated ROI. Most analytic heavy programmes fail because companies don't take the time to figure out if their current data assets will help them meet their strategic goals. Both components involved in the collection, storage, and consumption of business process outputs, which may be an application output file, log, database, or teams that businesses use to collect data, are referred to as data properties.

The ability to use both internal and external data is a required skill for solving business challenges and preventing the insanity of doing the same action and expecting different outcomes. Only leaders who have a razor-sharp ability to ingest and analyse data-rich environments and accurately convert it into functional strategic decisions will be able to command potential market leadership and competitive supremacy.

Inefficient data auditing processes are a common cause of data analytics programmes failing. Startups create auditing solutions to examine data collection, storage, protection, and processing in order to improve data effectiveness. The solutions assist businesses in developing data usage guidelines that increase revenue by maximising the use of all available data properties.

Nakala Analytics, a Kenyan startup, provides data inventory solutions for data-driven innovation. The company assists in determining if current data assets are adequate for successful innovation. Furthermore, Nakala Analytics detects anomalies and the data's potential, as well as evaluating the efficiency of data collection.

Ensure access to quality

It's important that your team has access to full and reliable data. This is what allows you to analyse in-scope business processes in great detail. However, when it comes to data analytics, this is also the most daunting hurdle to overcome. The front end is where the heavy lifting takes place: defining data, ensuring its accuracy, completeness, and availability, as well as standardising it as required. Internal audit departments that are in charge of internal auditing always start with a single business process (for example, procurement) and work with IT to explain the data. They use business intelligence software, data warehouses, and other tools that are already in use in other areas of the company to continuously provide data to internal audit.

Team work

Some are worried that emerging technology tools (automation) and the experts required to help them would result in job losses in internal audit. You can be assured that the internal auditor's job is secure. There isn't, and never will be, a replacement for their intelligence and judgement. Auditors, on the other hand, must have a strong knowledge of evidence as well as the ability to think critically. They must be able to rapidly understand emerging business processes and use data analytics to develop procedures to mitigate the threats they've found. They must also be taught how to use analytics software and resources. Internal audit groups that are best in class take various approaches to developing an analytics-ready internal audit programme.

Conclusion

Internal audit has the potential to be transformed by data analytics, elevating the position to a senior strategic role within the company. CAEs, on the other hand, must take great care to ensure that implementation is achieved in a calculated, thoughtful, and well-planned way, with the right technology resources, staff, and training in place.

If you're just getting started with any data innovation project, now is a perfect time to perform a Data Audit for Data Innovation. This is the first and most effective step towards data monetization and data value realization.

Email us: corporate@nakala-analytics.co.ke

The truth about leveraging your historical data to navigate the COVID-19 pandemic unfortunately needs some wisdom

2020-04-20T14:20:20+03:00

The coronavirus (COVID-19) pandemic has had a significant impact on business all over the world. The effects of corona are likely to affect many businesses negatively. While you may face different challenges, it is imperative to respond rapidly in order to remain standing assuming this pandemic progresses for 12 more months.

The importance of data today cannot be over-emphasized. The truth about leveraging your historical data to navigate the COVID-19 pandemic unfortunately needs some wisdom:

While you may have silos of historical structured and unstructured data, that history might not tell you anything about how COVID-19 will affect your business in the near future.
Doing analysis using your existing data repositories may not satisfactorily handle the fast-moving and unknown variables of an outbreak like COVID-19.
Seasonality patterns of your forecasts may not come into play this year and hence, all your pre-computed statistical forecasts may not tell you the truth about the next 10 months.

As time passes by, you are likely to run out of options, and sooner or later it may be necessary to PAUSE. PAUSE is a Problem-Solving Framework and an acronym for a cycle of five steps: Perceive, Ask, Understand, Strategize, and Evaluate. The secret to PAUSE lies in the data you have collected since the first discovery of COVID 19 pandemic in your country. The use of recent data will bring everyone to the table and on a daily basis help your business to:

Perceive: The PAUSE process begins with learning more about the situation by observing, listening, and asking questions. As business tends to slow down seek to learn more. It is important to observe and listen to your customers and not to apply industry wide strategic restrictions.

Ask: Ask questions to learn more about what is happening to your lowest affected segments in addition to careful listening and observation. Seek to answer questions from the pandemic data collected. Do not hesitate to sample and find out what’s happening to your high value customers.

Understand: Understand your customer experience or viewpoint. Relationships are the core underpinning factors to monitor in order to manage your business effectively. In order for your business relationship to function smoothly, consider each party's point of view and act accordingly. Do not generalize. Generalized models kick out high valued prospects because they fall into risky categories.

Strategize: After analyzing all information gathered (also referred to as the pandemic data) select and take decisive actions treating each of your customers uniquely.

Evaluate the outcomes on a daily basis using reflective processes.

As you are implementing PAUSE, the following data strategies may help you and your business take decisive.

Credit Team: Seek to reduce exposure by increasing your customer profile scoring threshold and treating customer complaints on an individual basis. Consistently make use of data to deliver credit scores, models, and tools to ease the business exposure during this time.

Customer Service Team: Reach out to your clients; Let them know you care, accommodate their needs according to their risk profiles and increase the customer contact ratio. Consistently gather and analyze your customer data, in order to get daily actionable insights that allow you to evaluate your strategy on a daily basis.

HR Team: Communicate, Educate & Encourage. Consistently track capability by using capability analytics metrics. The success of your business depends on the level of expertise and skill. Seek to identify core competencies you need in your business and ensure these are available all the time. As an analytics team, we shall be compiling and sharing strategic data insights to help you navigate the COVID 19 pandemic. If you would like short term remote help to manage your analytics operations, please reach out.

Key Points to consider before investing in big data analytics

2019-12-20T15:25:26+03:00

Big data is not a luxury, it is a necessity. Technological advances of course have played a major role in collection and generation of more and more data. Data on its own is not valuable, what is valuable are the insights that can be drawn from this data after suitable analysis has been conducted. Below are 5 key points that organizations need to consider before investing in big data analytics.

· Clear objectives

Organizations looking to invest in big data and analytics need to be very clear on what business needs big data and analytics are going to solve. At the onset, organizations with no set objectives will suffer many losses due to the risky nature of such an undertaking.

· Change Management

One advantage that will accrue to an organization investing in big data analytics is predictive analytics. Understanding customers’ tastes and preferences, ability to predict demand more accurately is every business’ headache. Understanding customers’ tastes and preferences, will lead to production of goods and services that will meet the customers’ tastes and preferences. The results – increased revenue, reduced cost and impressive bottom-line for business.

· Human Capital

Data scientists and other professionals in the data field possess the necessary skills required to analyze big data sets. Organizations should therefore identify these professionals. Their remuneration should match industry standards and their value-add should not be underestimated.

· Investment Decision

Boardroom conversations will always involve the ROI question. Will our investment in big data result in a positive ROI. The answer is YES! I mean, the ability to predict demand more accurately, flexibility of price continuum. Our data strategy approach at Nakala Analytics will ensure that more time and money is not wasted in creating systems that will not result in a positive ROI.

· Privacy

One of the challenges in big data analytics is privacy. Most of the data is normally from the customer and therefore the organization needs to ensure that this information about the customer is not divulged. However there is the EU GDPR, the Data Protection Law in Kenya which carries a set of principles of ethics and clearly specifies the penalties associated with breach of the rules.

Nella Oluoch

nella@nakala-analytics.co.ke