Ian  Robinson

Ian Robinson

1623992220

Data Onboarding: Survey Reveals Major Import Challenges

The data onboarding process must be improved and simplified to keep up with exponential data growth and rapid development of new SaaS products.

It seems as though new software products and applications hit the market weekly, even daily. These applications help increase productivity and efficiency and can make everyday tasks much more straightforward. But for any new application to work, it needs data – which has become a currency in the way we frequently exchange it. We can’t benefit from applications without data. Software applications are simply empty boxes until data is migrated over. Hence, data onboarding becomes ever-more important.

A customer can’t use a company’s product and get value from it if their data doesn’t import correctly into the product. For example, a marketer can’t effectively use a marketing automation solution without their leads. A business can’t sell eCommerce products without SKUs. An HR representative can’t properly use a payroll solution without accurate employee profiles.

A recently conducted survey to determine the state of data onboarding, or the process of importing data, was completed by more than 100 businesses across industries to gather input. The survey results reveal that more than 90 percent of respondents must transfer data from one system to another to run their businesses. Yet, more than three-quarters of these respondents cited that they “sometimes” or “often” run into problems with the data onboarding process.

See also: Data Onboarding: Overcoming the Challenge

#big data #data management #saas #data onboarding: survey reveals major import challenges #data onboarding #survey reveals major import challenges

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Data Onboarding: Survey Reveals Major Import Challenges
Ian  Robinson

Ian Robinson

1623992220

Data Onboarding: Survey Reveals Major Import Challenges

The data onboarding process must be improved and simplified to keep up with exponential data growth and rapid development of new SaaS products.

It seems as though new software products and applications hit the market weekly, even daily. These applications help increase productivity and efficiency and can make everyday tasks much more straightforward. But for any new application to work, it needs data – which has become a currency in the way we frequently exchange it. We can’t benefit from applications without data. Software applications are simply empty boxes until data is migrated over. Hence, data onboarding becomes ever-more important.

A customer can’t use a company’s product and get value from it if their data doesn’t import correctly into the product. For example, a marketer can’t effectively use a marketing automation solution without their leads. A business can’t sell eCommerce products without SKUs. An HR representative can’t properly use a payroll solution without accurate employee profiles.

A recently conducted survey to determine the state of data onboarding, or the process of importing data, was completed by more than 100 businesses across industries to gather input. The survey results reveal that more than 90 percent of respondents must transfer data from one system to another to run their businesses. Yet, more than three-quarters of these respondents cited that they “sometimes” or “often” run into problems with the data onboarding process.

See also: Data Onboarding: Overcoming the Challenge

#big data #data management #saas #data onboarding: survey reveals major import challenges #data onboarding #survey reveals major import challenges

Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Umeng Analytics & Push Flutter Plugins

Umeng Analytics&Push Flutter Plugins(umeng_analytics_push) 

  • Language: English | 中文简体
  • Umeng API: umeng:analytics & umeng:push
  • Tip: From v2.1.0 supported Umeng "Compliance Guide" Android IOS, and made appropriate adjustments to facilitate integration.
  • Note: The following document description shall prevail, do not refer to the settings in the example

Usages

Import

dependencies:
  umeng_analytics_push: ^x.x.x #The latest version is shown above, plugin1.x supports flutter1.x, plugin2.x supports flutter2.x

# Or import through Git (choose one, Git version may be updated more timely)

dependencies:
  umeng_analytics_push:
      git:
        url: https://github.com/zileyuan/umeng_analytics_push.git

Android pretreatment settings (with Kotlin example)

Create a custom FlutterApplication class as the startup class, if the push function is not needed, uemng_message_secret is set to null or ""

package com.demo.umeng.app

import io.flutter.app.FlutterApplication
import io.github.zileyuan.umeng_analytics_push.UmengAnalyticsPushFlutterAndroid

class MyFlutterApplication: FlutterApplication() {
    override fun onCreate() {
        super.onCreate();
        UmengAnalyticsPushFlutterAndroid.androidPreInit(this, "uemng_app_key", "channel", "uemng_message_secret")
    }
}

Modify MainActivity, add Umeng settings

package com.demo.umeng.app

import android.os.Handler
import android.os.Looper
import android.content.Intent
import androidx.annotation.NonNull;
import io.flutter.embedding.android.FlutterActivity
import io.flutter.embedding.engine.FlutterEngine
import io.flutter.plugins.GeneratedPluginRegistrant
import io.github.zileyuan.umeng_analytics_push.UmengAnalyticsPushFlutterAndroid
import io.github.zileyuan.umeng_analytics_push.UmengAnalyticsPushPlugin

class MainActivity: FlutterActivity() {
    var handler: Handler = Handler(Looper.myLooper())

    override fun configureFlutterEngine(@NonNull flutterEngine: FlutterEngine) {
        GeneratedPluginRegistrant.registerWith(flutterEngine);
    }

    override fun onNewIntent(intent: Intent) {
        // Actively update and save the intent every time you go back to the front desk, and then you can get the latest intent
        setIntent(intent);
        super.onNewIntent(intent);
    }

    override fun onResume() {
        super.onResume()
        UmengAnalyticsPushFlutterAndroid.androidOnResume(this)
        if (getIntent().getExtras() != null) {
            var message = getIntent().getExtras().getString("message")
            if (message != null && message != "") {
                // To start the interface, wait for the engine to load, and send it to the interface with a delay of 5 seconds
                handler.postDelayed(object : Runnable {
                    override fun run() {
                        UmengAnalyticsPushPlugin.eventSink.success(message)
                    }
                }, 5000)
            }
        }
    }

    override fun onPause() {
        super.onPause()
        UmengAnalyticsPushFlutterAndroid.androidOnPause(this)
    }
}

Modify the AndroidManifest.xml file

<application
  android:name="com.demo.umeng.app.MyFlutterApplication">
</application>

Add the vendor push channel, see the official documentation for details umeng:push:vendor

Modify MyFlutterApplication

package com.demo.umeng.app

import io.flutter.app.FlutterApplication
import io.github.zileyuan.umeng_analytics_push.UmengAnalyticsPushFlutterAndroid

class MyFlutterApplication: FlutterApplication() {
    override fun onCreate() {
        super.onCreate();
        UmengAnalyticsPushFlutterAndroid.androidInit(this, "uemng_app_key", "channel", "uemng_message_secret")
        // Register Xiaomi Push (optional)
        UmengAnalyticsPushFlutterAndroid.registerXiaomi(this, "xiaomi_app_id", "xiaomi_app_key")
        // Register Huawei Push (optional, need add other infomation in AndroidManifest.xml)
        UmengAnalyticsPushFlutterAndroid.registerHuawei(this)
        // Register Oppo Push (optional)
        UmengAnalyticsPushFlutterAndroid.registerOppo(this, "oppo_app_key", "oppo_app_secret")
        // Register Vivo Push (optional, need add other infomation in AndroidManifest.xml)
        UmengAnalyticsPushFlutterAndroid.registerVivo(this)
        // Register Meizu Push (optional)
        UmengAnalyticsPushFlutterAndroid.registerMeizu(this, "meizu_app_id", "meizu_app_key")
    }
}

Modify the AndroidManifest.xml, fill in the real id or key

<application
  android:name="com.demo.umeng.app.MyFlutterApplication">
    <!-- Vivo push channel start (optional) -->
    <meta-data
        android:name="com.vivo.push.api_key"
        android:value="vivo_api_key" />
    <meta-data
        android:name="com.vivo.push.app_id"
        android:value="vivo_app_id" />
    <!-- Vivo push channel end-->

    <!-- Huawei push channel start (optional) -->
    <meta-data
        android:name="com.huawei.hms.client.appid"
        android:value="appid=huawei_app_id" />
    <!-- Huawei push channel end-->
</application>

Use the following parameters to send, accept offline messages

"mipush": true
"mi_activity": "io.github.zileyuan.umeng_analytics_push.OfflineNotifyClickActivity"  

If the App needs to use proguard for obfuscated packaging, please add the following obfuscated code

-dontwarn com.umeng.**
-dontwarn com.taobao.**
-dontwarn anet.channel.**
-dontwarn anetwork.channel.**
-dontwarn org.android.**
-dontwarn org.apache.thrift.**
-dontwarn com.xiaomi.**
-dontwarn com.huawei.**
-dontwarn com.meizu.**

-keepattributes *Annotation*

-keep class com.taobao.** {*;}
-keep class org.android.** {*;}
-keep class anet.channel.** {*;}
-keep class com.umeng.** {*;}
-keep class com.xiaomi.** {*;}
-keep class com.huawei.** {*;}
-keep class com.meizu.** {*;}
-keep class org.apache.thrift.** {*;}

-keep class com.alibaba.sdk.android.** {*;}
-keep class com.ut.** {*;}
-keep class com.ta.** {*;}

-keep public class **.R$* {
    public static final int *;
}

IOS pretreatment settings (with Swift example)

Modify AppDelegate.swift file

import UIKit
import Flutter

@UIApplicationMain
@objc class AppDelegate: FlutterAppDelegate {
    override func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplication.LaunchOptionsKey: Any]?) -> Bool {
        GeneratedPluginRegistrant.register(with: self)
        UmengAnalyticsPushFlutterIos.iosPreInit(launchOptions, appkey:"uemng_app_key", channel:"appstore");
        return super.application(application, didFinishLaunchingWithOptions: launchOptions)
    }

    // If you need to handle Push clicks, use the following code
    @available(iOS 10.0, *)
    override func userNotificationCenter(_ center: UNUserNotificationCenter, didReceive response: UNNotificationResponse, withCompletionHandler completionHandler: @escaping () -> Void) {
        let userInfo = response.notification.request.content.userInfo
        UmengAnalyticsPushFlutterIos.handleMessagePush(userInfo)
        completionHandler()
    }
}

Modify Runner-Bridging-Header.h file

#import "GeneratedPluginRegistrant.h"
#import <UMCommon/UMCommon.h>
#import <UMCommon/MobClick.h>
#import <UMPush/UMessage.h>
#import <UserNotifications/UserNotifications.h>
#import <umeng_analytics_push/UmengAnalyticsPushIos.h>

Use in Flutter

Initialize Umeng, call it after agreeing to the "Privacy Policy" according to the "Compliance Guide", two parameter switches, one is log, the other is push

import 'package:umeng_analytics_push/umeng_analytics_push.dart';

UmengAnalyticsPush.initUmeng(false, true);

Click Push response

import 'package:umeng_analytics_push/umeng_analytics_push.dart';
import 'package:umeng_analytics_push/message_model.dart';

UmengAnalyticsPush.addPushMessageCallback((MessageModel message) {
  print("UmengAnalyticsPush Message ======> $message");
});

Operation Alias

import 'package:umeng_analytics_push/umeng_analytics_push.dart';

UmengAnalyticsPush.addAlias('1001', 'jobcode');
UmengAnalyticsPush.setAlias('1002', 'jobcode');
UmengAnalyticsPush.deleteAlias('1002', 'jobcode');

Operation Tags

import 'package:umeng_analytics_push/umeng_analytics_push.dart';

UmengAnalyticsPush.addTags('manager');
UmengAnalyticsPush.deleteTags('manager');

Page buried point operation

import 'package:umeng_analytics_push/umeng_analytics_push.dart';

UmengAnalyticsPush.pageStart('memberPage');
UmengAnalyticsPush.pageEnd('memberPage');

Custom event

import 'package:umeng_analytics_push/umeng_analytics_push.dart';

UmengAnalyticsPush.event('customEvent', '1000');

Use this package as a library

Depend on it

Run this command:

With Flutter:

 $ flutter pub add umeng_analytics_push

This will add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):

dependencies:
  umeng_analytics_push: ^2.1.3

Alternatively, your editor might support or flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:umeng_analytics_push/umeng_analytics_push.dart';

example/lib/main.dart

import 'package:flutter/material.dart';

void main() => runApp(MyApp());

class MyApp extends StatefulWidget {
  @override
  _MyAppState createState() => _MyAppState();
}

class _MyAppState extends State<MyApp> {

  @override
  void initState() {
    super.initState();
  }

  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      home: Scaffold(
        appBar: AppBar(
          title: const Text('Plugin example app'),
        ),
        body: Center(
        ),
      ),
    );
  }
} 

Download Details:

Author: zileyuan

Source Code: https://github.com/zileyuan/umeng_analytics_push

#flutter #analytics 

Ruth  Nabimanya

Ruth Nabimanya

1625057520

The Growing Need and Importance of Data Enrichment

Organizations can make progress by enriching their customer data to improve their customer experience.

As 2021 initiates, customers have sky-high expectations with regards to their experiences with each business they associate with: retail brands, utility services, and even their banks. It’s anticipated that these organizations should envision our necessities, know what our identity is, and consistently be important. Basically, we need organizations to read our thoughts. While this is incomprehensible and unreasonable, organizations can make progress by enriching their customer data to improve their customer experience.

Having access to the same volunteered data your rivals have, you don’t have a lot of favorable advantages over them. To improve and get more profound information, you can utilize data enrichment.

Data enrichment is a process that can transform the data you have into a total profile that precisely maps the requirements of your leads.

Why Data Enrichment?

Impact of Data Enrichment

#big data #latest news #the growing need and importance of data enrichment #data enrichment #importance #importance of data enrichment